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題名：使用加權移動視窗模式發掘前K個有利益的項目集
書刊名：明新學報
作者：蔡秀滿
作者(外文)：Tsai, Pauray S. M.
出版日期：2015
卷期：41:1
頁次：頁23-36
主題關鍵詞：資料探勘；頻繁項目集；有利益的項目集；加權移動視窗；Data mining；Frequent itemset；Profitable itemset；Weighted sliding window
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傳統的頻繁項目集探勘將所有項目的效益皆視為相同，因此只能發掘交易物品間的相關性，無法找出較有利益的項目集。近幾年來，高效益項目集探勘已成為資料探勘領域中一項重要的研究課題。高效益項目集探勘的研究是以項目集的利潤和數量當作考量的要素，然而探勘結果所得到的高效益項目集卻無法呈現項目間的數量關係，因此在決策支援上的效益將會受到限制。本研究以項目集的支持度取代數量，將項目集的支持度和平均利潤當作有利益的項目集之考慮要素，並且以加權移動視窗模式為基礎，提出一個在交易資料流環境中發掘前k個有利益的項目集之方法。本研究設計一個只讀取資料流一次的演算法，並且將每次探勘過程所儲存的資訊在下一次探勘中再利用，有效率地從交易資料流中發掘前k個有利益的項目集。
Traditional researches on frequent itemset mining do not consider the profit of each item. Thus they cannot discover high utility itemsets. Recently, high utility mining has become an important research topic. However, the mining result cannot present the relationships among items' quantities. As a result, the utility on decision making is limited. This paper uses ＂support＂ to replace ＂quantity＂. We propose a single pass algorithm for mining top-k profitable itemsets based on the weighted sliding window model. The developed algorithm takes advantage of reusing stored information to efficiently discover all the top-k profitable itemsets in the transactional data stream.
期刊論文
1.Ahmed, C. F.、Tanbeer, S. K.、Jeong, B. S.、Lee, Y. K.(2009)。Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases。IEEE Transactions on Knowledge and Data Engineering，21(12)，1708-1721。  new window
2.Barber, B.、Hamilton, H. J.(2003)。Extracting Share Frequent Itemsets with Infrequent Subsets。Data Mining and Knowledge Discovery，7，153-185。  new window
3.Chi, Y、Wang, H.、Yu, P. S.、Muntz, R. R.(2006)。Catch the Moment: Maintaining Closed Frequent Itemsets over a Data Stream Sliding Window。Knowledge and Information Systems，7(3)，265-294。  new window
4.Chu, C. J.、Tseng, V. S.、Liang, T.(2008)。An Efficient Algorithm for Mining Temporal High Utility Itemsets from。Data Streams, The Journal of Systems and Software，81(1)，1105-1117。  new window
5.Grahne G.、Zhu, J.(2005)。Fast Algorithms for Frequent Itemset Mining Using FP-Trees。IEEE Transactions on Knowledge and Data Engineering，17(10)，1347-1362。  new window
6.Hong, T. P.、Lee, C. H.、Wang, S. L.(2011)。Effective Utility Mining with the Measure of Average Utility。Expert Systems with Applications，38，8259-8265。  new window
7.Jiang, N.、Gruenwald, L.(2006)。Research Issues in Data Stream Association Rule Mining。SIGMOD Record，35(1)，14-19。  new window
8.Lin, M.Y.、Tu, T.F.、Hsueh, S.C.(2012)。High utility pattern mining using the maximal itemset property and lexicographic tree structures。Information Sciences，215，1-14。  new window
9.Pillai, J.、Vyas, O. P.(2010)。Overview of Itemset Utility Mining and its Applications。International Journal of Computer Applications，5(11)，9-13。  new window
10.Li, Y. C.、Yeh, J. S.、Chang, C. C.(2008)。Isolated items discarding strategy for discovering high utility itemsets。Data & Knowledge Engineering，64(1)，198-217。  new window
會議論文
1.Erwin, A.、Gopalan, R. P.、Achuthan, N. R.(2007)。CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach71-76。  new window
2.Erwin, A.、Gopalan, R. P.、Achuthan, N. R.(2008)。Efficient Mining of High Utility Itemsets from Large Datasets。Pacific-Asia Conference on Knowledge Discovery and Data Mining，554-561。  new window
3.Hong, T. P.、Lee, C. H.、Wang, S. L.(2009)。An Incremental Mining Algorithm for High Average-Utility Itemsets。International Symposium on Pervasive Systems, Algorithms, and Networks，421-425。  new window
4.Lee, C.-H.、Lin, C.-R.、Chen, M.-S.(2001)。Sliding-Window Filtering: An Efficient Algorithm for Incremental Mining。The International Conference on Information and Knowledge Management，263-270。  new window
5.Li, H. F.、Huang, H. Y.、Chen, Y. C.、Liu, Y. J.、Lee, S. Y.(2008)。Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams。The 8th IEEE International Conference on Data Mining。Washington, DC。881-886。  new window
6.Liu, Y.、Liao, W.K.、Choudhary, A.(2005)。A Two Phase Algorithm for Fast Discovery of High Utility Itemsets.。Pacific-Asia Conference on Knowledge Discovery and Data Mining。  new window
7.Palmemin, P.、Orlando, S.、Perego, R.(2004)。Statistical Properties of Transactional Databases。ACM Symposium on Applied Computing。  new window
8.Tanbeer, S. K.、Ahmed, C.F.、Jeong, B. S.、Lee, Y. K.(2008)。CP-Tree: A Tree Structure for Single Pass Frequent Pattern Mining。Pacific-Asia Conference on Knowledge Discovery and Data Mining。  new window
9.Tseng, V. S.、Wu, C. W.、Shie, B. E.、Yu, P. S.(2010)。UP-Growth: An Efficient Algorithm for High Utility Itemset Mining。The 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining。Washington, DC。  new window
10.Yao, H.、Hamilton, H. I.、Butz, C. J.(2004)。A Foundational Approach to Mining Itemset Utilities from Databases。The 2004 SIAM International Conference on Data Mining。  new window
11.Manku, G. S.、Motwani, R.(2002)。Approximate frequency counts over data streams。International conference on very large data bases，346-357。  new window
12.Agrawal, R.、Srikant, R.(1994)。Fast algorithms for mining association rules in large database。The 20th International Conference on Very Large Data Bases。Morgan Kaufmann Publishers Inc.。478-499。  new window
研究報告
1.Tsai, P. S. M.(2013)。Efficient Mining of High Utility Itemsets in Data Streams。  new window
其他
1.Shie, B. E.，Tseng, V. S.，Yu, P. S.(2010)。Online Mining of Temporal Maximal Utility Itemsets from Data Streams。  new window
圖書論文
1.Giannella, C.、Han, J.、Pei, J.、Yan, X.、Yu, P. S.(2003)。Mining Frequent Patterns in Data Streams at Multiple Time Granularities。Next generation data mining。  new window
 
 
 
 
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