臺灣博碩士論文加值系統

隨著電子資訊化的潮流，企業每天營運活動中都會產生或收集到大量資料，包括生產、客戶、原料等各方面的資料，加上資料流通的管道也邁向多元化、無線化、高速化；因此，現今企業已不再是資訊不足，而是如何從堆積如山的資料中，淬煉出有用的資訊，面對即將來臨的知識經濟世代中做好準備，提高公司的競爭力。
『資訊勘測』，意指從大量的資料中去尋找新的資訊或獲取新的知識。自從R. Agrawal, T. Imielinski 和　A.Swami 等人於　Proceeding of ACM SIGMOD, pages 207-216, May 1993 提出『Mining Association Rules between Sets of Items in Large Databases』以來，資訊勘測研究已經廣泛地受到國內外業學界的高度重視，相
關的消費資料分析也已經應用在傳統交易市場。
儘管如此，傳統資訊勘測法仍然有許多不及之處。於是乎，淬煉出之資訊往往仍然無法切重使用者之需求，或流於冗述，抑或是偏離市場需求時效性。有鑑於此，本論文將針對資訊更新性、資訊加權性、資訊時效性、少量多次消費行為，以及動態篩選資訊等等機制，提出個人見解與功能性之補強。
具體而言，於本論文之撰寫研究過程中，吾人首先致力於觀察分析傳統演算法於實際資料庫系統中之運作與功能瓶頸，進而提出新而有效之資訊勘測環境模型，繼而提出創新之資訊勘測演算法。其中之具體貢獻將至少包括﹔時序性勘測模型、加權式勘測模型、因果律分析模型、分割漸進式演算法（ＳＷＦ，ＰＷＭ，和ＰＷＭ），以及動態篩選機制之網際勘測。以期拉近資訊勘測演算法則與實際應用之距離。
資訊勘測一個起源於1980年代末期，每年擁有數百億美元的潛在商績的嶄新領域，於1990年代吸引數以萬計的研究人員投入，以每年上千篇研究論文的發表方式，成長茁壯，亦將於未來綻放光芒。雖然資訊勘測已經有很多成功的故事，但是那能只是與實驗室研究成果相去不遠的結果。一個能放之四海皆準的成功突破，端賴而今而後之持續研究。

Since the early work in algorithm Apriori, several efficient algorithms to mine association rules have been developed. These studies cover a broad spectrum of topics including: (1) fast algorithms based on the level-wise Apriori framework, partitioning, sampling, parallel methods, TreeProjection and FP-growth methods; (2) incremental updating; (3) mining of generalized multi-dimensional and multi-level rules; (4) mining of quantitative rules; (5) constraint-based rule mining and multiple minimum supports issues; (6) temporal association rule discovery; and (7) episode mining. While these are important results toward enabling the integration of association mining and fast searching algorithms, we note that these mining methods cannot effectively be applied to the mining of a large incremental temporal database which is of increasing popularity recently. Specifically, some phenomena are observed when we take the issues of Incremental Updates, Weighted Transactions, Publication-like Items, Short Transactions, and Dynamic Thresholds into consideration.
In view of this, we explore in this thesis an effective sliding-window filtering (abbreviatedly as SWF) algorithm for incremental mining of association rules. Under SWF, the cumulative information of mining previous partitions is selectively carried over toward the generation of candidate itemsets for the subsequent partitions. Algorithm SWF not only significantly reduces I/O and CPU cost by the concepts of cumulative filtering and scan reduction techniques but also effectively controls memory utilization by the technique of sliding-window partition. By utilizing proper scan reduction techniques, only one scan of the incremented dataset is needed by algorithm SWF.
Furthermore, without fully considering the time-variant characteristics of items and transactions, it is noted that some discovered rules may be expired from users’ interest. In other words, some discovered knowledge may be obsolete and of little use, especially when we perform the mining schemes on a transaction database of short life cycle products. This aspect is, however, rarely addressed in prior studies. In view of this, we broaden in this thesis the horizon of frequent pattern mining by introducing a weighted model of transaction-weighted association rules in a time-variant database. Specifically, we propose an efficient Progressive Weighted Miner (abbreviatedly as PWM) algorithm to perform the mining for this problem as well as conduct the corresponding performance studies.
In addition, in this thesis, we explore a new problem of mining general temporal association rules in publication databases. In essence, a publication database is a set of transactions where each transaction T is a set of items of which each item contains an individual exhibition period. The current model of association rule mining is not able to handle the publication database due to the following fundamental problems, i.e., (1) lack of consideration of the exhibition period of each individual item; (2) lack of an equitable support counting basis for each item. To remedy this, we propose an innovative algorithm Progressive-Partition-Miner (abbreviatedly as PPM) to discover general temporal association rules in a publication database. Explicitly, the execution time of PPM is, in orders of magnitude, smaller than those required by other competitive schemes which are directly extended from existing methods.
On the other hand, it is noted that the existing models of rule mining might not be able to discover user preferred frequent patterns efficiently due to the following two fundamental problems: (1) the puzzles for mining association rules on a short transaction database; (2) lack of long patterns for sequential pattern mining. To remedy this, this thesis explores the mining of causality rules. The causality rule explored in this dissertation consists of a sequence of triggering events and a set of consequential events, and is designed with the capability of mining non-sequential, inter-transaction information across multiple categories. Hence, the causality rule mining provides a very general framework for rule derivation.
Moreover, with the fast increase in Web activities, mining of path traversal patterns plays an essential role in the Web mining. While existing methods are efficient for the mining of frequent path traversal patterns from the access information contained in a log file, these approaches are likely to over evaluate associations. Explicitly, most previous studies of mining path traversal patterns are based on the model of a uniform support threshold, where a single support threshold is used to determine frequent traversal patterns without taking into consideration such important factors as the length of the pattern, the positions of Web pages, and the importance of a particular pattern, etc. In reality, however, a Web page at a lower level of a Web site naturally has a lower occurrence frequency than their corresponding higher level concepts, e.g., the portal Web page. As a result, a low support threshold will lead to lots of uninteresting patterns derived whereas a high support threshold may cause some interesting patterns with lower supports to be ignored. Hence, to capture the very nature of the Web mining problems, it is desirable to have a more general model for the support threshold. This thesis broadens the horizon of frequent path traversal pattern mining by introducing a flexible model of mining Web traversal patterns with dynamic thresholds. Specifically, we explore a new data mining capability which involves mining path traversal patterns with the concept of dynamic thresholds in a time-variant Web environment.

1 Introduction
1.1 Motivation
1.1.1 Apriori-like algorithms
1.1.2 Partition-based algorithms
1.1.3 FUP-based algorithms
1.1.4 FP-based algorithms
1.2 Overview of the Dissertation
1.2.1 Incremental Updates
1.2.2 Weighted Transactions
1.2.3 Publication-like Items
1.2.4 Short Transactions
1.2.5 Dynamic Thresholds
1.3 Organization of the Dissertation
2 Sliding Window Filtering: An Efficient Method for Incremental Mining on a Time-Variant Database
2.1 Introduction
2.2 Preliminaries and Related Works
2.2.1 Apriori-like algorithms
2.2.2 Partition-based algorithms
2.2.3 FUP-based algorithms
2.3 SWF: Incremental Mining with Sliding-Window Filtering
2.3.1 An example of incremental mining by SWF
2.3.2 Algorithm of SWF
2.3.3 Correctness of SWF
2.4 Experimental Studies
2.4.1 Generation of synthetic workload
2.4.2 Experiment one: Relative performance
2.4.3 Experiment two: Evaluation of I/O cost
2.4.4 Experiment three: Reduction of CPU and memory overhead 39
2.4.5 Experiment four: Scaleup performance
2.5 Summary
3 Progressive Weighted Miner: An Efficient Method for Time-Constraint Mining on a Time-Variant Database
3.1 Introduction
3.2 Problem Description
3.3 Progressive Weighted Mining .
3.3.1 Algorithm of PWM .
3.3.2 An illustrative example of algorithm PWM
3.3.3 Correctness of PWM
3.4 Performance Studies
3.4.1 Generation of synthetic workload
3.4.2 Experiment one: Relative performance
3.4.3 Experiment two: Evaluation of I/O cost and CPU overhead 3.4.4 Experiment three: Scaleup performance
3.5 Summary
4 Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules
4.1 Introduction
4.2 Problem Description
4.2.1 Preliminaries
4.2.2 Traversing the Search Space
4.3 Mining General Temporal Association Rules
4.3.1 Algorithm of PPM
4.3.2 An illustrative example of algorithm PPM
4.3.3 Correctness of PPM
4.4 Experimental Studies
4.4.1 Generation of synthetic workload
4.4.2 Experiment one: Relative performance
4.4.3 Experiment two: Evaluation of I/O cost and CPU overhead
4.4.4 Experiment three: Scaleup performance
4.5 Summary
5 Causality Rules: Exploring the Relationship between Triggering and Consequential Events in a Database of Short Transactions
5.1 Introduction
5.2 Problem Description
5.2.1 First Phase: Discovering One-Triggering Causality Rules
5.2.2 Second Phase: Generating Multi-Triggering Causality Rules 5.2.3 An Illustrative Example for the Mining of Causality Rules
5.3 Algorithms for Mining One-Triggering Causality Rules
5.3.1 Hierarchical Matching
5.3.2 Algorithm HMC: Candidate-Sets-Based Hierarchical Matching 5.3.3 Algorithm HMD: Data-Sets-Based Hierarchical Matching
5.3.4 Algorithm HMA: Adaptive Hierarchical Matching
5.4 Experimental Studies
5.4.1 Generation of Synthetic Data
5.4.2 Relative Performance
5.4.3 Scale-up
5.4.4 Causality Rules Generation
5.5 Summary
6 Efficient Algorithms for On-line Web Log Mining with Dynamic Thresholds
6.1 Introduction
6.2 Basic Principles
6.2.1 Problem Description
6.2.2 Search Space Traversed by Algorithm UTM
6.3 DTM: Dynamic Threshold Miner
6.3.1 Algorithm DTM
6.3.2 Algorithm Incremental DTM (IDTM)
6.4 Performance Studies
6.4.1 Generation of synthetic workload
6.4.2 Performance Comparison between DTM and UTM
6.4.3 Scale-Up Experimental Results on IDTM
6.5 Summary
7 Conclusions

@article{lincr02,
author = "C.-R. Lin and C.-H. Lee and M.-S. Chen and P. S. Yu",
title = "{Distributed Data Mining in a Chain Store Database of Short Transactions}",
journal = "Proc. of the 8th ACM SIGKDD Intern''l Conf. on Knowledge Discovery and Data Mining (KDD-2002)",
year = Ŗ002;"
}
@article{leepp02,
author = "C.-H. Lee and M.-S. Chen and C.-R. Lin",
title = "{Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules}",
journal = "accepted by IEEE Trans. on Knowledge and Data Engineering",
year = Ŗ002;"
}
@article{changii01,
author = "C.H. Chang and S.C. Lui",
title = "{IEPAD: Information Extraction based on Pattern Discovery}",
journal = "Proc. of the tenth International Conference on World Wide Web",
year = Ŗ001;"
}
@article{changap01,
author = "C.H. Chang and S.C. Lui and Y.C. Wu",
title = "{Applying Pattern Mining to Web Information Extraction}",
journal = "Proc. of Pacific-Asia Conference on Knowledge Discovery and Data Mining",
year = Ŗ001;"
}
@article{yenae01,
author = "S.-J. Yen and C.-W. Cho",
title = "{An Efficient Approach for Updating Sequential Patterns Using Database Segmentation}",
journal = "International Journal of Fuzzy Systems Special Issue on Soft Computing and Data Mining",
pages = 虶-431",
year = Ŗ001;"
}
@article{yenag01,
author = "S.-J. Yen and A.L.P. Chen",
title = "{A Graph-Based Approach for Discovering Various Types of Association Rules}",
journal = "IEEE Transactions on Knowledge and Data Engineering",
pages = 蠗-845",
year = Ŗ001;"
}
@article{hsudr98,
author = "C.-N. Hsu and C.A. Knoblock",
title = "{Discovering robust knowledge from databases that change}",
journal = "Data Mining and Knowledge Discovery",
pages = ඍ-95",
year = 薗8;"
}
@article{liuet99,
author = "C.C. Liu and J.L. Hsu and A.L.P. Chen",
title = "{Efficient Theme and Non-Trivial Repeating Pattern Discovering in Music Databases}",
journal = "Proc. IEEE International Conference on Data Engineering",
year = 薗9;"
}
@article{leeeg99,
author = "K.L. Lee and G. Lee and A.L.P. Chen",
title = "{Efficient Graph-Based Algorithms for Discovering and Maintaining Knowledge in Large Databases}",
journal = "Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining",
year = 薗9;"
}
@article{tsaiae99,
author = "P.S.M. Tsai and C.C. Lee and A.L.P. Chen",
title = "{An Efficient Approach for Incremental Association Rule Mining}",
journal = "Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining",
year = 薗9;"
}
@article{leecr02,
author = "C.-H. Lee and P. S. Yu and M.-S. Chen",
title = "{Causality Rules: Exploring the Relationship between Triggering and Consequential Events in a Database of Short Transactions}",
journal = "Proc. of the 2nd SIAM Intern''l Conference on Data Mining",
month = "April",
year = Ŗ002;"
}
@BOOK{handm00,
author = "J. Han and M. Kamber",
title = "{Data Mining: Concepts and Techniques}",
publisher = "Morgan Kaufmann Publishers",
year = Ŗ000;"
}
@ARTICLE{bayacb993,
author = "R. J. Bayardo and R. Agrawal and D. Gunopulos",
title = "{Constraint-Based Rule Mining in Large, Dense Databases}",
journal = "Proceedings of the 15th International Conference on Data Engineering",
pages = 薌-197",
year = 薗9;",
month = "March"
}
@inproceedings{ozdeca98,
author = "Banu Ozden and Sridhar Ramaswamy and Abraham Silberschatz",
title = "Cyclic Association Rules",
booktitle = "{ICDE}",
pages = 虬-421",
year = 薗8;"
}
@article{liuic98,
author = "B. Liu and W. Hsu and Y. Ma",
title = "Integrating classification and association rule mining",
mining. In Proc. 4th Int. Conf. Knowledge Discovery and Data Mining (KDD''98),
pages 80--86, New York, 1998.",
year = 薗8;"
}
@inproceedings{sarais98,
author = "Sunita Sarawagi and Shiby Thomas and Rakesh Agrawal",
title = "Integrating association rule mining with relational database systems: alternatives and implications",
pages = 蘧--354",
booktitle = "ACM-SIGMOD"
year = 薗8;",
url = "citeseer.nj.nec.com/sarawagi98integrating.html" }
@article{zhenrw01,
author = "Z. Zheng and R. Kohavi and L. Mason",
title = "Real World Performance of Association Rule Algorithms",
journal="ACM SIGKDD",
year = Ŗ001;"
}
@article{zaanat01,
author = "O. Zaane and M. Antonie",
title = "Automatic Text Categorization using Association Rule Mining",
journal= " The Journal of Intelligent Information
Systems, Special Issue on Automated Text Categorization, 2001",
year = Ŗ001;" }
@article{blocsu99,
author = "Hendrik Blockeel and Luc De Raedt and Nico Jacobs and Bart Demoen",
title = "Scaling Up Inductive Logic Programming by Learning from Interpretations",
journal = "Data Mining and Knowledge Discovery",
volume = ŗ",
number = ŕ",
pages = ඃ-93",
year = 薗9;"
}
@article{jrcb00,
author = "Roberto J. Bayardo Jr. and Rakesh Agrawal and Dimitrios Gunopulos",
title = "Constraint-Based Rule Mining in Large, Dense Databases",
journal = "Data Mining and Knowledge Discovery",
volume = Ř",
number = Ŗ/3",
pages = 薩-240",
year = Ŗ000;"
}
@article{zakisp99,
author = "M. Zaki and C. Ho and R. Agrawal",
title = "Scalable parallel classification for data mining on shared memory multiprocessors",
journal = "Proceedings of IEEE International Conference on Data Engineering.",
year = 薗9;"
}
@article{of98th,
author = "D. of and S. Indices and f WWW",
title = "Daily Prediction of Stock Market Indices from Textual WWW Data",
journal = "Conference on Knowledge Discovery and Data Mining, to appear, New York, 1998.
(This paper is also invited for IEEE Int Conf on SMC, 1998)",
year = 薗8;" }
@inproceedings{kornrr98,
author = "Flip Korn and Alexandros Labrinidis and Yannis Kotidis and Christos Faloutsos",
title = "Ratio Rules: {A} New Paradigm for Fast, Quantifiable Data Mining",
booktitle = "Proc. 24th Int. Conf. Very Large Data Bases, {VLDB}",
month = Ŗ4;--27~",
pages = 蜖--593",
year = 薗8;"
}
@article{simomc01,
author = "Ke Wang Simon",
title = "Mining Confident Rules Without Support Requirement",
journal="the 10th ACM International Conference on Information and Knowledge Management (CIKM 2001), Atlanta"
url = "citeseer.nj.nec.com/456252.html" }
@article{aggafa99,
author = "C. Aggarwal and C. Procopiuc and J. Wolf and P. Yu",
title = "Jong Soo Park: Fast Algorithms for Projected Clustering",
journal = "Proc. ACM SIGMOD",
year=薗9;"
}
@inproceedings{aggaan99,
author = "Charu C. Aggarwal and Joel L. Wolf and Philip S. Yu",
title = "A new method for similarity indexing of market basket data",
pages = 虧--418",
journal = "Proc. ACM SIGMOD",
year = 薗9;"
}
@inproceedings{agraas98,
author = "Rakesh Agrawal and Johannes Gehrke and Dimitrios Gunopulos and Prabhakar Raghavan",
title = "Automatic subspace clustering of high dimensional data for data mining applications",
pages = ඦ--105",
journal = "Proc. ACM SIGMOD",
year = 薗8;"
}
@inproceedings{brinbm97,
author = "Sergey Brin and Rajeev Motwani and Craig Silverstein",
title = "Beyond market baskets: generalizing association rules to correlations",
pages = 藙--276",
journal = "Proc. ACM SIGMOD",
year = 薗7;"
}
@inproceedings{brindi97,
author = "Sergey Brin and Rajeev Motwani and Jeffrey D. Ullman and Shalom Tsur",
title = "Dynamic itemset counting and implication rules for market basket data",
journal = "Proc. ACM SIGMOD",
pages = 藏--264",
year = 薗7;",
url = "citeseer.nj.nec.com/brin97dynamic.html"}
@article{bchndi98,
author = "A. Bchner and M. Mulvenna",
title = "Discovering Internet Marketing Intelligence through Online Analytical Web
Usage Mining",
journal = "ACM SIGMOD",
year = 薗8;"
}
@inproceedings{esteic98,
author = "Martin Ester and Hans-Peter Kriegel and Jorg Sander and Michael Wimmer and Xiaowei Xu",
title = "Incremental Clustering for Mining in a Data Warehousing Environment",
booktitle = "Proc. 24th Int. Conf. Very Large Data Bases, {VLDB}",
month = Ŗ4;--27~",
pages = 蘓--333",
year = 薗8;"
}
@article{silvbm98,
author = "Craig Silverstein and Sergey Brin and Rajeev Motwani",
title = "Beyond Market Baskets: Generalizing Association Rules to Dependence Rules",
journal = "Data Mining and Knowledge Discovery",
volume = Ŗ",
number = ŕ",
pages = ŗ9;-68",
year = 薗8;"
}
@article{sheiam98,
author = "G. Sheikholeslami and S. Chatterjee and A. Zhang",
title = "WaveCluster: A multi-Resolution Clustering Approach for Very Large Spatial
Databases",
journal = " Very Large Spatial Databases, Proc. of 24th Intl.
Conf. On Very Large Data Bases.",
pages = 虼-439",
year = 薗8;"
}
@Article{yanged01,
author = {C. Yang and U. Fayyad and P. Bradley},
title = {Efficient Discovery of Error-Tolerant Frequent Itemsets in High Dimensions},
journal = {The Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = Ŗ001;"
}
@article{leesf01,
author = "C.-H. Lee and C.-R. Lin and M.-S. Chen",
title = "{Sliding-Window Filtering: An Efficient Algorithm for Incremental Mining}",
journal = "Proc. of the ACM 10th Intern''l Conf. on Information and Knowledge Management",
month ="November",
year = Ŗ001;"
}
@article{savamf98,
author = {A. Savasere and E. Omiecinski and S.B. Navathe},
title = {Mining for Strong Negative Associations in a Large Database of Customer Transactions},
booktitle = {Proceedings of the Fourteenth International Conference on Data Engineering},
publisher = {IEEE Computer Society},
year = {1998},
pages = {494-502}
}
@article{liuma99,
author = "B. Liu and W. Hsu and Y. Ma",
title = "{Mining Association Rules with Multiple Minimum Supports}",
journal = "Proc. of 1999 Int. Conf. on Knowledge Discovery and Data Mining",
pages = "",
year = 薗9;",
month = "August"
}
@article{bettmt98,
author = "C. Bettini and X.S. Wang and S. Jajodia",
title = "{Mining Temporal Relationships with Multiple Granularities in Time Sequences}",
journal = "Bulletin of the IEEE Computer Society Technical Committee on Data Engineering",
pages = "",
year = 薗8;"
}
@article{aleaa00,
author = "J.M. Ale and G. Rossi",
title = "{An Approach to Discovering Temporal Association Rules}",
journal = "ACM Symposium on Applied Computing",
pages = "",
year = Ŗ000;"
}
@article{chendo98,
author = "X. Chen and I. Petrounias and H. Heathfield",
title = "{Discovery of Association Rules in Temporal Databases}",
journal = "Proc. of Issues and Applications of Database Technology",
pages = "",
year = 薗8;"
}
@article{tansdo98,
author = "A.U. Tansel and N.F. Ayan",
title = "{Discovery of Association Rules in Temporal Databases}",
journal = "Proc. of AAAI on Knowledge Discovery in Databases",
pages = "",
year = 薗8;"
}
@article{chendt00,
author = "X. Chen and I. Petr",
title = "{Discovering Temporal Association Rules: Algorithms, Language and System}",
journal = "Proc. of 2000 Int. Conf. on Data Engineering",
pages = "",
year = Ŗ000;",
month = ""
}
@ARTICLE{cohefi01,
author = {E. Cohen et.al.},
title = "{Finding Interesting Associations without Support Pruning}",
year = 2001,
journal = {IEEE Transactions on Knowledge and Data Engineering},
pages = {64-78},
month = {January/February}
}
@article = {bettini98mining,
author = "Claudio Bettini and Xiaoyang Sean Wang and Sushil Jajodia",
title = "Mining Temporal Relationships with Multiple Granularities in Time Sequences",
journal = "Data Engineering Bulletin",
volume = Ŗ1;",
number = ŕ",
pages = ŗ2;-38",
year = 薗8;",
url = "citeseer.nj.nec.com/bettini98mining.html"
}
@article{houtsm953,
author = "M. Houtsma and A. Swami",
title = "{Set-Oriented Mining for Association Rules in Relational Databases}",
journal = "Proceedings of the 11th International Conference on Data Engineering",
pages = Ŗ5;-33",
year = 薗5;",
month = "March"
}
@ARTICLE{agrapm9612,
author = {R. Agrawal and J.C. Shafer},
title = "{Parallel Mining of Association Rules: Design, Implementation, and Experience}",
year = 1996,
journal = {IEEE Transactions on Knowledge and Data Engineering},
pages = {487-499},
month = {December}
}
@article{srikmq96,
author = "R. Srikant and R. Agrawal",
title = "{Mining quantitative association rules in large relational tables}",
journal = "Proc. of 1996 ACM-SIGMOD Conf. on Management of Data",
pages = "",
year = 薗6;",
month = ""
}
@article{thomae97,
author = "S. Thomas and S. Bodagala and K. Alsabti and S. Ranka",
title = "{An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases}",
journal = "Proc. of 1997 Int. Conf. on Knowledge Discovery and Data Mining",
pages = "",
year = 薗7;",
month = ""
}
@article{ayanae99,
author = "N.F. Ayan and A.U. Tansel and E. Arkun",
title = "{An Efficient Algorithm to Update Large Itemsets with Early Pruning}",
journal = "Proc. of 1999 Int. Conf. on Knowledge Discovery and Data Mining",
pages = "",
year = 薗9;",
month = ""
}
@Article{Hippaf00,
author = {J. Hipp and U. G\"untzer and G. Nakhaeizadeh},
title = {Algorithms for Association Rule Mining -- A General Survey and Comparison},
journal = {SIGKDD Explorations},
year = 2000,
volume = 2,
number = 1,
pages = {58-64},
month = {July},
url = "citeseer.nj.nec.com/hipp00algorithms.html"
}
@inproceedings = {thomim00,
author = "S. Thomas and S. Chakravarthy",
title = "Incremental Mining of Constrained Associations",
booktitle = "HiPC",
pages = 蛳-558",
year = Ŗ000;",
url = "citeseer.nj.nec.com/301407.html"
}
@article{ngem98,
author = "L.V.S. Lakshmanan and R.T. Ng and J. Han and A. Pang",
title = "{Exploratory Mining and Pruning Optimization of Constrained Associations Rules}",
journal = "Proc. of 1998 ACM-SIGMOD Conf. on Management of Data",
pages = "",
year = 薗8;",
month = ""
}
@article{peipm01,
author = "J. Pei and J. Han and L.V.S. Lakshmanan",
title = "{Mining Frequent Itemsets with Convertible Constraints}",
journal = "Proc. of 2001 Int. Conf. on Data Engineering",
pages = "",
year = Ŗ001;",
month = ""
}
@article{wangbh99,
author = "K. Wang and S.Q. Zhou and S.C. Liew",
title = "{Building Hierarchical Classifiers Using Class Proximity}",
journal = "Proc. of 1999 Int. Conf. on Very Large Data Bases",
pages = 蘻-374",
year = 薗9;",
month = ""
}
@article{cohefi00,
author = "E.Cohen and M. Datar and S. Fujiwara and A. Gionis and P. Indyk and R. Motwani and J.D. Ullman and C. Yang",
title = "{Finding Interesting Associations without Support Pruning}",
journal = "Proc. of 2000 Int. Conf. on Data Engineering",
pages = 蚹-499",
year = Ŗ000;",
month = ""
}
@article{laksoo99,
author = "L. V. S. Lakshmanan and R. Ng and J. Han and A. Pang",
title = "{Optimization of Constrained Frequent Set Queries with 2-Variable Constraints}",
journal = "Proc. of 1999 ACM-SIGMOD Conf. on Management of Data",
pages = 蕭-168",
year = 薗9;",
month = "June"
}
@article{hancm99,
author = "J. Han and L. V. S. Lakshmanan and R. T. Ng",
title = "{Constraint-Based, Multidimensional Data Mining}",
journal = "COMPUTER (special issues on Data Mining)",
pages = Ř6;-50",
year = 薗9;",
month = ""
}
@article{peicw00,
author = "J. Pei and J. Han",
title = "{Can We Push More Constraints into Frequent Pattern Mining?}",
journal = "Proc. of 2000 Int. Conf. on Knowledge Discovery and Data Mining",
pages = "",
year = Ŗ000;",
month = "August"
}
@article{wangmf00,
author = "K. Wang and Y. He and J. Han",
title = "{Mining Frequent Itemsets Using Support Constraints}",
journal = "Proc. of 2000 Int. Conf. on Very Large Data Bases",
pages = "",
year = Ŗ000;",
month = "September"
}
@article{hanmf0012,
author = "J. Han and J. Pei",
title = "{Mining Frequent Patterns by Pattern-Growth: Methodology and Implications}",
journal = "ACM SIGKDD Explorations (Special Issue on Scaleble Data Mining Algorithms)",
pages = "",
year = Ŗ000;",
month = "December"
}
@article{tungcc01,
author = "A. K. H. Tung and J. Han and L. V. S. Lakshmanan and R. T. Ng",
title = "{Constraint-Based Clustering in Large Databases}",
journal = "Proc. of 2001 Int. Conf. on Database Theory",
pages = "",
year = Ŗ001;",
month = "January"
}
@article{hanff00,
author = "J. Han and J. Pei and B. Mortazavi-Asl and Q. Chen and U. Dayal and M.-C. Hsu",
title = "{FreeSpan: Frequent pattern-projected sequential pattern mining}",
journal = "Proc. of 2000 Int. Conf. on Knowledge Discovery and Data Mining",
pages = 蘳-359",
year = Ŗ000;",
month = "August"
}
@article{agarat00,
author = "R. Agarwal and C. Aggarwal and V.V.V. Prasad",
title = "{A Tree Projection Algorithm for Generation of Frequent Itemsets}",
journal = "Jornal of Parallel and Distributed Computing (Special Issue on High Performance Data Mining)",
pages = "",
year = Ŗ000;",
month = ""
}
@article{muelfs95,
author = "A. Mueller",
title = "{Fast Sequential and Parallel Algorithms for Association Rule Mining: A Comparison}",
journal = "Technical Report CS-TR-3515, Dept. of Computer Science, Univ. of Maryland, College Park, MD",
pages = "",
year = 薗5;",
month = ""
}
@article{hanmf00,
author = "J. Han and J. Pei and Y. Yin",
title = "{Mining Frequent Patterns without Candidate Generation}",
journal = "Proc. of 2000 ACM-SIGMOD Int. Conf. on Management of Data",
pages = 蚶-493",
year = Ŗ000;",
month = "May"
}
@article{linma98,
author = "J.-L. Lin and M.H. Dunham",
title = "{Mining Association Rules: Anti-Skew Algorithms}",
journal = "Proc. of 1998 Int''l Conf. on Data Engineering",
pages = 蚶-493",
year = 薗8;",
month = ""
}
@article{cheuag974,
author = "D. Cheung and S.D. Lee and B. Kao",
title = "{A General Incremental Technique for Updating Discovered Association Rules}",
journal = "Proc. International Conference On Database Systems For Advanced Applications",
pages = "",
year = 薗7;",
month = "April"
}
@article{cheumo962,
author = "D. Cheung and J. Han and V. Ng and C.Y. Wong",
title = "{Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique}",
journal = "Proc. of 1996 Int''l Conf. on Data Engineering",
pages = 蔺-114",
year = 薗6;",
month = "February"
}
@article{hanmf005,
author = "J. Han and J. Pei and Y. Yin",
title = "{Mining Frequent Patterns without Candidate Generation}",
journal = "Proc. of 2000 ACM-SIGMOD Int. Conf. on Management of Data",
pages = "",
year = Ŗ000;",
month = "May"
}
@article{srikmg959,
author = "R. Srikant and R. Agrawal",
title = "{Mining Generalized Association Rules}",
journal = "Proc. of the 21th International Conference on Very Large Data Bases",
pages = 虧-419",
year = 薗5;",
month = "September"
}
@article{ngea949,
author = "R.T. Ng and J. Han",
title = "{Efficient and Effective Clustering Methods for Spatial Data Mining}",
journal = "Proc. of the 18th International Conference on Very Large Data Bases",
pages = 蕠-155",
year = 薗4;",
month = "September"
}
@INPROCEEDINGS{hanem993,
author = {J. Han and G. Dong and Y. Yin},
title = "{Efficient Mining of Partial Periodic Patterns in Time Series Database}",
year = 薗9;",
pages = {106-115},
booktitle = {Proc. of the 15th International Conference on Data Engineering},
month = {March}
}
@article{agrams953,
author = "R. Agrawal and R. Srikant",
title = "{Mining Sequential Patterns}",
journal = "Proc. of the 11th International Conference on Data Engineering",
pages = ŗ-14",
year = 薗5;",
month = "March"
}
@article{parkma97,
author = "J.-S. Park and M.-S. Chen and P. S. Yu",
title = "{Mining Association Rules with Adjustable Accuracy}",
journal = "Proc. of the ACM 6th Intern''l Conf. on Information and Knowledge Management",
year = 薗7;",
month = "November",
pages = 蕧-160"
}
@article{agrama935,
author = "R. Agrawal and T. Imielinski and A. Swami",
title = "{Mining Association Rules between Sets of Items in Large Databases}",
journal = "Proc. of ACM SIGMOD",
pages = 薟-216",
year = 薗3;",
month = "May"
}
@ARTICLE{chened984,
author = {M.-S. Chen and J.-S. Park and P. S.Yu},
title = "{Efficient Data Mining for Path Traversal Patterns}",
year = 1998,
journal = {IEEE Transactions on Knowledge and Data Engineering},
volume = 10,
pages = {209-221},
number = 2,
month = {April}
}
@ARTICLE{chendm9612,
author = {M.-S. Chen and J. Han and P. S.Yu},
title = "{Data Mining: An Overview from Database Perspective}",
year = 1996,
journal = {IEEE Transactions on Knowledge and Data Engineering},
volume = 8,
pages = {866-883},
number = 6,
month = {December}
}
@article{agrafa949,
author = "R. Agrawal and R. Srikant",
title = "{Fast Algorithms for Mining Association Rules in Large Databases}",
journal = "Proc. of the 20th International Conference on Very Large Data Bases",
pages = 蚮-499",
year = 1994,
month = "September"
}
@article{hando959,
author = "J. Han and Y. Fu",
title = "{Discovery of Multiple-Level Association Rules from Large Databases}",
journal = "Proc. of the 21th International Conference on Very Large Data Bases",
pages = 虴-431",
year = 薗5;",
month = "September"
}
@article{mann947,
author = "H.~Mannila and H.~Toivonen and A.~Inkeri Verkamo",
title = "{Efficient Algorithms for Discovering Association Rules}",
journal = "Proc. of AAAI Workshop on Knowledge Discovery
in Databases",
pages = 薅-192",
year = "July, 1994"
}
@article{parkua9710,
author = "J.-S. Park and M.-S. Chen and P. S. Yu",
title = "{Using a Hash-Based Method with Transaction Trimming for Mining Association Rules}",
journal = "IEEE Transactions on Knowledge and Data Engineering",
pages = 蟽-825",
VOLUME = ŝ",
NUMBER = ř",
year = 薗7;",
month = "October"
}
@article{savaae959,
author = "A.~Savasere and E.~Omiecinski and S.~Navathe",
title = "{An Efficient Algorithm for Mining Association Rules in Large Databases}",
journal = "Proc. of the 21th International Conference on Very Large Data Bases",
pages = 蚀-444",
year = 薗5;",
month = "September"
}
@article{gantmv99,
author = {V. Ganti and J. Gehrke and R. Ramakrishnan},
title = "{Mining Very Large Databases}",
journal = {Computer, IEEE},
pages = {38-45},
month = {Aug},
year = {1999}
}
@article{brindi975,
AUTHOR = "S. Brin and R. Motwani and J. D. Ullman and S. Tsur",
TITLE = "{Dynamic Itemset Counting and Implication Rules for Market Basket Data}",
journal = "ACM SIGMOD Record",
pages = 藏-264",
volume = Ŗ6;",
number = Ŗ",
MONTH = "May",
YEAR = 1997
}
@article{toivsl969,
author = "H. Toivonen",
title = "{Sampling Large Databases for Association Rules}",
journal = "Proc. of the 22th VLDB Conference",
pages = 蕖-145",
year = 薗6;",
month = "September"
}
@article{parkae955,
author = "J.-S. Park and M.-S. Chen and P. S. Yu",
title = "{An Effective Hash Based Algorithm for
Mining Association Rules}",
journal = "Proc. of ACM SIGMOD",
pages = 蕿-186",
year = "May, 1995"
}
@article{wangtt01,
author = "W. Wang, J. Yang, R.R. Muntz",
title = "{TAR: Temporal Association Rules on Evolving Numerical Attributes}",
journal = "Proc. of 2000 Int. Conf. on Data Engineering",
pages = "",
year = Ŗ001;",
month = ""
}
@Article{ayadim01,
author = {A.M. Ayad and N.M. El-Makky and Y. Taha},
title = {Incremental mining of constrained association rules},
journal = {Proc. of the First SIAM Conference on Data Mining},
year = Ŗ001;"
}
@article{leeom01,
author = "C.-H. Lee and C.-R. Lin and M.-S. Chen",
title = "{On Mining General Temporal Association Rules in a Publication Database}",
journal = "Proc. of 2001 IEEE International Conference on Data Mining",
month ="November",
year = Ŗ001;"
}
@Article{manndo01,
author = {H. Mannila and D. Rusakov},
title = {Decomposition of Event Sequences into Independent Components},
journal = {Proc. of the First SIAM Conference on Data Mining},
year = Ŗ001;"
}
@Article{mannfs01,
author = {H. Mannila and J. Sepponen},
title = {Finding similar situations in sequences of events via random projections},
journal = {Proc. of the First SIAM Conference on Data Mining},
year = Ŗ001;"
}
@article{manndg96,
author = "Heikki Mannila and Hannu Toivonen",
title = "Discovering Generalized Episodes Using Minimal Occurrences",
journal = "Proc. of 1996 Int. Conf. on Knowledge Discovery and Data Mining",
pages = 蕢",
year = 薗6;",
url = "citeseer.nj.nec.com/mannila96discovering.html"
}
@article{manndo97,
author = "Heikki Mannila and Hannu Toivonen and A. Inkeri Verkamo",
title = "Discovery of Frequent Episodes in Event Sequences",
journal = "Data Mining and Knowledge Discovery",
volume = ŕ",
number = ŗ",
pages = 藓-289",
year = 薗7;",
url = "citeseer.nj.nec.com/mannila97discovery.html"
}
@article{bussla98,
author = "S.R. Buss and P.N. Yianilos",
title = "{Linear and Time Minimum-Cost Matching Algorithms for Quasi-Convex Tours}",
journal = "SIAM Journal on Computing",
pages = 蕺-201",
year = 薗8;",
month = ""
}
@article{arkiot99,
author = "E.M. Arkin and Y.J. Chiang and J.S.B. Mitchell and S.S. Skiena and T.C. Yang",
title = "{On the Maximum Scatter Traveling Salesperson Problem}",
journal = "SIAM Journal on Computing",
pages = 蛓-544",
year = 薗9;",
month = ""
}
@article{cheram00,
author = "J. Cheriyan and R. Thurimella",
title = "{Approximating Minimum-Size k-Connected Spanning Subgraphs via Matching}",
journal = "SIAM Journal on Computing",
pages = 蛠-560",
year = Ŗ000;",
month = ""
}
@article{gonzac95,
author = "J. Gonzalez and O. Landaeta",
title = "{A Competitive Strong Spanning Tree Algorithm for the Maximum Bipartite Matching Problem}",
journal = "SIAM Journal on Computing",
pages = 薊-195",
year = 薗5;",
month = ""
}
@article{peimf01,
author = "J. Pei and J. Han and L.V.S. Lakshmanan",
title = "{Mining Frequent Itemsets with Convertible Constraints}",
journal = "Proc. of 2001 Int. Conf. on Data Engineering",
pages = "",
year = Ŗ001;",
month = ""
}
@article{sedga88,
author = "R. Sedgewick",
title = "{Algorithms}",
journal = "Addison-Wesley, second edition",
pages = "",
year = ŕ988;",
month = ""
}
@article{leshsf00,
author = "N. Lesh and M.J. Zaki and M. Oglhara",
title = "{Scalable feature mining for sequential data}",
journal = "IEEE Intelligent Systems [see also IEEE Expert]",
pages = Ř8; -56",
year = Ŗ000;",
month = "March-April"
}
@INPROCEEDINGS{agrama933,
author = {R. Agrawal and T. Imielinski and A. Swami},
title = "{Mining Associations between Sets of Items in Massive Databases}",
year = 1993,
pages = {207-216},
booktitle = {Proceeding of ACM SIGMOD},
month = {May}
}
@article{wangtt01,
author = "W. Wang, J. Yang, R.R. Muntz",
title = "{TAR: Temporal Association Rules on Evolving Numerical Attributes}",
journal = "Proc. of 2000 Int. Conf. on Data Engineering",
pages = "",
year = Ŗ001;",
month = ""
}
@Article{wangem00,
author = {W. Wang and J. Yang and P.S. Yu},
title = "{Efficient mining of weighted association rules (WAR)}",
journal ={Proc. of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = Ŗ000;"
}
@ARTICLE{zakisa00,
author = {M.J. Zaki},
title = "{Scalable Algorithms for Association Mining}",
year = 2000,
journal = {IEEE Transactions on Knowledge and Data Engineering},
pages = {372-390},
}
@ARTICLE{zakiem99,
author = {M.J. Zaki},
title = "{Parallel and Distributed Association Mining: A Survey}",
year = 1999,
journal = {IEEE Concurrency, special issue on Parallel Mechanisms for Data Mining},
pages = {14-25},
month= "December"
}
@ARTICLE{cheuem96,
author = {D.W. Cheung, V. Ng, A. W. Fu, Y. Fu},
title = "{Efficient Mining of Association Rules in Distributed Databases}",
year = 1996,
journal = {IEEE Transactions on Knowledge and Data Engineering},
pages = {911-922},
}
@Article{keogei01,
author = {E. Keogh and S. Chu and M. Pazzani},
title = {Ensemble-Index: A New Approach to Indexing Large Databases},
journal = {Proc. of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = Ŗ001;"
}
@Article{liudu01,
author = {B. Liu and Y. Ma and P.S. Yu},
title = {Discovering Unexpected Information from Your Competitors'' Web Sites},
journal = {Proc. of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = Ŗ001;"
}
@article{keogla01,
author = "E. Keogh and K. Chakrabarti and S. Mehrotra and M. Pazzani",
title = "{Locally adaptive dimensionality reduction for indexing large time series databases}",
journal = "Proc. of 2001 ACM-SIGMOD Conf. on Management of Data",
pages = "",
year = Ŗ001;",
month = ""
}
@article{cheram00,
author = "J. Cheriyan and R. Thurimella",
title = "{Approximating Minimum-Size k-Connected Spanning Subgraphs via Matching}",
journal = "SIAM Journal on Computing",
pages = 蛠-560",
year = Ŗ000;",
month = ""
}
@article{gonzac95,
author = "J. Gonzalez and O. Landaeta",
title = "{A Competitive Strong Spanning Tree Algorithm for the Maximum Bipartite Matching Problem}",
journal = "SIAM Journal on Computing",
pages = 薊-195",
year = 薗5;",
month = ""
}
@Article{liudu01,
author = {B. Liu and Y. Ma and P.S. Yu},
title = {Discovering Unexpected Information from Your Competitors'' Web Sites},
journal = {Proc. of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = Ŗ001;"
}
@Article{wangem00,
author = {W. Wang and J. Yang and P.S. Yu},
title = "{Efficient mining of weighted association rules (WAR)}",
journal ={Proc. of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = Ŗ000;"
}
@article{mamm01,
author = "R. Mao, Y. Lu, J. Han",
title = "{Mining Multi-level and Multi-dimensional Frequent Patterns with Flexible Support Constraints}",
journal = "Proc. IEEE International Conference on Data Mining",
year = Ŗ001;"
}
@article{nanofg00,
author = "A. Nanopoulos and Y. Manolopoulos",
title = "{Finding Generalized Path Patterns for Web Log Data Mining}",
journal = "Proc. East-European Conference on Advanced Databases and Information System",
pages = 薧-228",
year = Ŗ000;"
}
@article{borgma98,
author = "J. Borges and M. Levene",
title = "{Mining Association Rules in Hypertext Databases }",
journal = "Proc. Conference on Knowledge Discovery and Data Mining (KDD''98)",
pages = 蕧-160",
year = 薗8;"
}
@article{cooldp99,
author = "R. Cooley and B. Mobasher and J. Srivastava",
title = "{Data Preparation for Mining World Wide Web Browsing Patterns }",
journal = "Knowledge and Information System",
pages = ř-32",
year = 薗9;"
}
@article{joshwa99,
author = "K. Joshi and A.Joshi and Y.Yesha and R. Krishnapuram",
title = "{Warehousing and Mining Web Logs }",
journal = "Proceedings Workshop on Web Information and Data Managenment",
pages = ඇ-68",
year = 薗9;"
}
@article{peima00,
author = "J. Pei and J. Han and B. Mortazavi-Asl and H.Zhu",
title = "{Mining Access Patterns Efficiently from Web Logs }",
journal = "Proceedings Pacific-Asia Conference on Knowledge Discovery and Data Mining
(PAKDD''00)",
pages = "",
year = Ŗ000;"
}
@article{zaiadw98,
author = "O. Zaiane and M. Xin and J. Han",
title = "{Discovering Web Access Patterns and Trands by Applying OLAP and Data Mining
Technology on Web Logs }",
journal = "Proceedings on Advances in Digital Libraries",
pages = ŕ9;-29",
year = 薗8;"
}
@article{mobawm96,
author = "B. Mobasher and N.Jain and E. Han and J. Srivastava ",
title = "{Web Mining: Pattern discovery from World Wide Web Tranactions Technical Report}",
journal = "TR 96-050, Department of Computer Science, University of Minnesota, Minneapolis",
pages = "",
year = 薗6;"
}
@article{peipms01,
author = "J. Pei and J. Han and B. Mortazavi-Asl and H. Pinto and Q. Chen and U. Dayal and M.-C. Hsu",
title = "{PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth}",
journal = "Proc. of 2001 Int. Conf. on Data Engineering",
pages = "",
year = Ŗ001;",
}