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研究生:王慶堯
研究生(外文):Ching-Yao Wang
論文名稱:利用準大項目集之漸進式資料挖掘
論文名稱(外文):Incremental Data Mining Using Pre-large Itemsets
指導教授:洪宗貝洪宗貝引用關係
指導教授(外文):Tzung-Pei Hong
學位類別:碩士
校院名稱:義守大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2000
畢業學年度:88
語文別:英文
論文頁數:66
中文關鍵詞:資料挖掘關連式法則大項目集準大項目集漸進式資料挖掘
外文關鍵詞:data miningassociation rulelarge itemsetpre-large itemsetincremental mining
相關次數:
  • 被引用被引用:16
  • 點閱點閱:528
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  • 下載下載:27
  • 收藏至我的研究室書目清單書目收藏:1
隨著大型資料庫與資料倉儲的廣泛應用,如何從這些龐大且複雜的資料當中萃取有用的資訊與知識,儼然已成為一個重要的研究領域。過去,研究者通常假設資料庫是靜態的以簡化資料萃取的問題,也因此大部份的傳統資料萃取演算法都屬於批次處理,而無法利用之前已萃取出來的資訊,來幫助從不斷成長的資料庫中作漸進式挖掘。因此發展一資料萃取演算法,能夠隨資料庫記錄不斷成長而漸進式地維護已萃取的資訊,對實際的應用相當重要。在本論文中,我們將提出『準大項目集(Pre-large itemset)』的概念,並根據此觀念設計兩個全新且有效率的漸進式演算法。所謂準大項目集,主要是由〝低支持度門檻值〞和〝高支持度門檻值〞兩個支持度門檻值所定義而成,以用來減少對原來資料庫的處理並節省維護所挖掘出知識庫的成本。準大項目集的運作如同一個間隔般用來降低任一大項目集(Large itemset)轉變成小項目集(Small itemset),或者是小項目集轉成大項目集的可能。因此我們所提出之演算法除非在間隔失去效用的情況下,否則將不需要對原來資料庫做處理或掃描的動作。另外,此演算法更具備一個特質,即當資料庫不斷的增加時,其效率會愈來愈好,這對於現實資料庫的應用尤其有用。
Due to the increasing usage of very large databases and data warehouses, mining useful information and helpful knowledge from transactions has been evolving into an important research area. In the past, researchers usually assumed the database was static to simplify the data-mining problem. Most of the classic algorithms proposed thus focused on batch mining, and did not utilize previously mined information for incrementally growing databases. In real-word applications, however, developing a mining algorithm that can incrementally maintain the discovered information as a database grows is quite important. In this thesis, we propose the concept of pre-large itemsets and design two novel efficient incremental mining algorithms based on it. Pre-large itemsets are defined using two support thresholds, a lower support threshold and an upper support threshold, to reduce rescanning the original databases and to save maintenance costs. Pre-large itemsets act like a gap, which reduces the movement of an itemset directly from large to small and vice verse.
In the proposed first algorithm, the lower support threshold is fixed and the number of new transactions allowed for not rescanning databases dynamically increases as databases grow. Thus, it doesn''t need to rescan the original database until a number of transactions have come. If the size of the database is growing larger, then the allowed number of new transactions will be larger too. In the second algorithm, the number of new transactions allowed for not rescanning databases is fixed, and the lower support threshold is dynamically set close to the upper support threshold as databases grow. Thus, as the size of the database is larger, the additional overhead decreases in maintaining the consistency of association rules with the updated databases. Therefore, along with the growth of a database, our proposed approaches are increasingly efficient. This characteristic is especially useful for real applications.
CHINESE ABSTRACT III
ENGLISH ABSTRACT IV
LIST OF FIGURES VII
LIST OF TABLES VIII
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 CONTRIBUTIONS 4
1.3 READER''S GUIDE 5
CHAPTER 2 REVIEW OF RELATED WORKS 6
2.1 DATA MINING PROCESS 6
2.2 MAINTENANCE OF ASSOCIATION RULES 7
2.3 THE FUP ALGORITHM 9
CHAPTER 3 PRE-LARGE ITEMSETS 15
CHAPTER 4 THEORETICAL FOUNDATIONS 18
CHAPTER 5 INCREMENTAL MINING ALGORITHM BY DYNAMICALLY INCREASING THE NEW TRANSACTION NUMBER 28
5.1 NOTATION 28
5.2 THE PROPOSED ALGORITHM BY DYNAMICALLY INCREASING THE NEW TRANSACTION NUMBER 29
5.3 AN EXAMPLE 32
CHAPTER 6 INCREMENTAL MINING ALGORITHM BY DYNAMICALLY RAISING THE LOWER SUPPORT THRESHOLD 41
6.1 NOTATION 41
6.2 THE PROPOSED ALGORITHM BY DYNAMICALLY RAISING THE LOWER SUPPORT THRESHOLD 42
6.3 AN EXAMPLE 45
CHAPTER 7 DISCUSSION AND CONCLUSION 54
REFERENCES 56
[1] R. Agrawal, T. Imielinksi and A. Swami, “Mining association rules between sets of items in large database,“ The ACM SIGMOD Conference, Washington DC, USA, 1993.
[2] R. Agrawal, T. Imielinksi and A. Swami, “Database mining: a performance perspective,” IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 6, pp. 914-925, 1993.
[3] R. Agrawal and R. Srikant, “Fast algorithm for mining association rules,” The International Conference on Very Large Data Bases, pp. 487-499, 1994.
[4] R. Agrawal and R. Srikant, ”Mining sequential patterns,” In 11th IEEE International Conference on Data Engineering, 1995.
[5] R. Agrawal, R. Srikant and Q. Vu, “Mining association rules with item constraints,” In 3th International Conference on Knowledge Discovery in Databases and Data Mining, Newport Beach, California, 1997.
[6] M.S. Chen, J. Han and P.S. Yu, “Data mining: An overview from a database perspective,” IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, December 1996.
[7] D.W. Cheung, J. Han, V.T. Ng, and C.Y. Wong, “Maintenance of discovered association rules in large databases: An incremental updating approach,” In 12th IEEE International Conference on Data Engineering, 1996.
[8] D.W. Cheung, S.D. Lee, and B. Kao, “A general incremental technique for maintaining discovered association rules,” In Proceedings of database systems for advanced applications, DASFAA’97, Melbourne, Australia, pp. 185-194, 1997.
[9] T. Fukuda, Y. Morimoto, S. Morishita and T. Tokuyama, "Mining optimized association rules for numeric attributes," The ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 182-191, 1996.
[10] J. Han and Y. Fu, “Discovery of multiple-level association rules from large database,” In 21th International Conference on Very Large Data Bases, Zurich, Swizerland, pp. 420-431, 1995.
[11] M.Y. Lin and S.Y. Lee, “Incremental update on sequential patterns in large databases,” In 10th IEEE International Conference on Tools with Artificial Intelligence, 1998.
[12] H. Mannila, H. Toivonen, and A. Inkeri Verkamo, “Efficient algorithm for discovering association rules,” Proceeding AAAI Workshop Knowledge Discovery in Databases, pp. 181-192, 1994.
[13] J.S. Park; M.S. Chen; P.S. Yu, “Using a hash-based method with transaction trimming for mining association rules,” IEEE Transactions on Knowledge and Data Engineering, Vol. 9, No. 5, pp.812-825, 1997.
[14] N.L. Sarda and N.V. Srinivas, “An adaptive algorithm for incremental mining of association rules,” In 9th International Workshop on Database and Expert Systems, 1998.
[15] R. Srikant and R. Agrawal, “Mining generalized association rules,” The 21th International Conference on Very Large Data Bases, Zurich, Swizerland, pp. 407-419, 1995.
[16] R. Srikant and R. Agrawal, “Mining quantitative association rules in large relational tables,” The 1996 ACM SIGMOD International Conference on Management of Data, Monreal, Canada, June, pp. 1-12, 1996.
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