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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李瑞庭 | |
| dc.contributor.author | Wan-Yu Weng | en |
| dc.contributor.author | 翁婉玉 | zh_TW |
| dc.date.accessioned | 2021-06-13T05:58:18Z | - |
| dc.date.available | 2006-07-03 | |
| dc.date.copyright | 2006-07-03 | |
| dc.date.issued | 2006 | |
| dc.date.submitted | 2006-06-27 | |
| dc.identifier.citation | [1] Agrawal, R., and Srikant, R., 1994. Fast algorithms for mining association rules. In: Proc. of Int. Conf. on Very Large Data Bases, pp. 487-499.
[2] Berberidis, C., Angelis, L., and Vlahavas, I., 2004. PREVENT: An algorithm for mining inter-transactional for the prediction of rare events. In: Proc. of the 2nd European Starting AI Researcher Symposium, IOS Press. [3] Brin, S., Motwani, R., Ullman, J., and Tsur, S., 1997. Dynamic itemset counting and implication rules for market basket data. In: Proc. of ACM-SIGMOD Int. Conf. on Management Of Data, pp. 255-264. [4] Burdick, D., Calimlim, M., and Gehrke, J., 2001. Mafia: A maximal frequent itemset algorithm for transactional databases. In: Proc. of the 17th Int. Conf. on Data Engineering, pp. 443-452. [5] Feng, L., Yu, J.X., Lu, H., and Han, J., 2002. A template model for multidimensional inter-transactional association rules. The VLDB Journal 11 (2), pp. 153-175. [6] Han, J., and Fu, Y., 1995. Discovery of multiple-level association rules from large databases. In: Proc. of Int. Conf. on Very Large Data Bases, pp. 420-431. [7] Han, J., and Kamber, M., 2000. Data mining: concepts and techniques, Morgan Kaufmann, San Francisco. [8] Han, J., Wang, J., Lu, Y., and Tzvetkov, P., 2002. Mining top-k frequent closed patterns without minimum support. In: Proc. of the 2nd Int. Conf. on Data Mining, pp. 211-218. [9] Huang , K.Y., Chang, C.H., and Lin, K.Z., 2005. ClosedPROWL: Efficient Mining of Closed Frequent Continuities by Projected Window List Technology. In: Proc. of the 5th SIAM International Conference on Data Mining. [10] Lu, H., Feng, L., and Han, J., 2000. Beyond intratransaction association analysis: mining multidimensional inter-transaction association rules. ACM Transactions on Information Systems 18 (4), pp. 423-454. [11] Lu, H., Han, J., and Feng, L., 1998. Stock movement prediction and n-dimensional inter-transaction association rules, In: Proc. of the 3rd ACM-SIGMOD Workshop on Research Issues on Data Mining and Knowledge, pp. 12:1-12:7. [12] Park, J.S., Chen, M.S., and Yu, P.S., 1995. An effective hash-based algorithm for mining association rules. In: Proc. of ACM-SIGMOD Int. Conf. on Management Of Data, pp. 175-186. [13] Pasquier, N., Bastide, Y., Taouil, R., and Lakhal, L., 1999. Discovering frequent closed itemsets for association rules. In: Proc. of the 5th Int. Conf. on Database Theory, pp. 398-416. [14] Pei, J., Han, J., and Mao, R., 2000. Closet: An efficient algorithm for mining frequent closed itemsets. In: Proc. of the 5th ACM-SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 11-20. [15] Savasere, A., Omiecinski, E., and Navathe, S., 1995. An efficient algorithm for mining association rules in large databases. In: Proc. of Int. Conf. on Very Large Data Bases, pp. 432-443. [16] Shenoy, P., Haritsa, J.R., Sudarshan, S., Bawa, M., Bhalotia, G., and Shah, D., 2000. Turbo-charging vertical mining of large databases. In: Proc. of ACM-SIGMOD Int. Conf. on Management of Data, pp. 22-33. [17] Singh, N.G., Singh, S.R., and Mahanta, A.K., 2005. CloseMiner: Discovering frequent closed itemsets using frequent closed tidsets. In: Proc. of the 5th Int. Conf. on Data Mining, pp. 633-636. [18] Srikant, R., and Agrawal, R., 1995. Mining generalized association rules. In: Proc. of Int. Conf. on Very Large Data Bases, pp. 407-419. [19] Srikant, R., and Agrawal, R., 1996. Mining quantitative association rules in large relational tables. In: Proc. of ACM-SIGMOD Int. Conf. on Management of Data, pp. 1-12. [20] Tung, A.K.H., Lu, H., Han, J., and Feng, L., 1999. Breaking the barrier of transactions: mining inter-transaction association rules. In: Proc. of ACM Int. Conf. on Knowledge and Data Discovery, pp. 423-454. [21] Tung, A.K.H., Lu, H., Han, J., and Feng, L., 2003. Efficient mining of intertransaction association rules. IEEE Transactions on Knowledge and Data Engineering 15 (1), pp. 43-56. [22] Wang, J., Han, J., and Pei, J., 2003. Closet+: Searching for the best strategies for mining frequent closed itemsets. In: Proc. of the 9th ACM-SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 236-245. [23] Zaki, M.J., 2000. Generating non-redundant association rules. In: Proc. of the 6th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 34-43. [24] Zaki, M. J., and Gouda, K., 2003. Fast vertical mining using diffsets. In: Proc. of the 9th ACM-SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 326-335. [25] Zaki, M.J., and Hsiao, C.J., 2005. Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Transactions on Knowledge and Data Engineering 17 (4), pp. 462-478. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/34210 | - |
| dc.description.abstract | 跨交易關聯規則可代表不同交易中項目間的關係，而近年來有愈來愈多相關的探勘演算法被提出，然而這些演算法會產生相當多的跨交易頻繁項目集合。找尋封閉性跨交易頻繁項目集合可使探勘的過程更有效率。
因此，在本篇論文中我們提出了一個探勘演算法叫「ICMiner」，以找尋封閉性跨交易頻繁項目集合。我們的方法可分為兩個階段。第一階段，將原始的資料庫轉換成領域屬性集合，使得每一個頻繁項目的領域屬性形成一個集合。第二階段，利用ID-tree去列舉出所有的封閉性跨交易頻繁項目集合。藉由ID-tree進行資料探勘，我們可以避免產生候選樣式及重複計算支持度。因此，ICMiner可大幅提升了找尋跨交易頻繁項目集合的效率。實驗結果顯示，ICMiner比FITI與ClosedPROWL快上幾十倍。 | zh_TW |
| dc.description.abstract | Many algorithms have been proposed recently for finding inter-transaction association rules, which represent the relationships among itemsets across different transactions. Since numerous frequent inter-transaction itemsets will be generated, mining closed frequent inter-transaction itemsets can speed up the mining process.
Therefore, in this thesis, we propose an algorithm, ICMiner (Inter-transaction Closed patterns Miner), to mine closed frequent inter-transaction itemsets. Our proposed algorithm consists of two phases. First, we convert the original transaction database into a set of domain attributes, datset, for each frequent item. Second, we enumerate closed frequent inter-transaction itemsets by using an itemset-datset tree, ID-tree. Mining closed frequent inter-transaction itemsets with an ID-tree, we can avoid costly candidate generation and repeatedly support counting. The experimental results show that our proposed algorithm outperforms the FITI and ClosedPROWL algorithms by one order of magnitude. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T05:58:18Z (GMT). No. of bitstreams: 1 ntu-95-R93725018-1.pdf: 474286 bytes, checksum: 00a7a214efc88025988bd2aac49a086d (MD5) Previous issue date: 2006 | en |
| dc.description.tableofcontents | Table of Contents i
List of Figures ii List of Tables iii Chapter 1 Introduction 1 Chapter 2 Problem Definition 3 Chapter 3 Mining Closed Frequent Inter-transaction Itemsets 6 3.1 Pruning Strategies 6 3.1.1 ID-tree and Joinable Class 6 3.1.2 Pruning Strategies 8 3.2 Join Operation 9 3.3 The ICMiner Algorithm 11 3.4 An Example 13 3.5 Diffsets for Optimization 16 Chapter 4 Performance Evaluation 19 4.1 Generation of Synthetic Data 19 4.2 Experiments on Synthetic Data 20 4.2.1 Basic Experiments 20 4.2.2 Scale-up Experiments 22 4.2.3 Effect of the Maximum Span 25 4.2 Experiments on Real Data 26 Chapter 5 Conclusions and Future Work 29 References 30 | |
| dc.language.iso | en | |
| dc.subject | 跨交易項目集合 | zh_TW |
| dc.subject | 資料探勘 | zh_TW |
| dc.subject | 關聯規則 | zh_TW |
| dc.subject | 封閉性項目集合 | zh_TW |
| dc.subject | association rules | en |
| dc.subject | inter-transaction itemsets | en |
| dc.subject | closed itemsets | en |
| dc.subject | data mining | en |
| dc.title | 封閉性跨交易頻繁項目集合之資料探勘 | zh_TW |
| dc.title | An Efficient Algorithm for Mining Closed Frequent Inter-transaction Itemsets | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 94-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳彥良,劉敦仁 | |
| dc.subject.keyword | 資料探勘,關聯規則,跨交易項目集合,封閉性項目集合, | zh_TW |
| dc.subject.keyword | data mining,association rules,inter-transaction itemsets,closed itemsets, | en |
| dc.relation.page | 32 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2006-06-28 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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