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題名：DSCP：在資料流環境下探勘連續性路徑型樣
書刊名：電子商務研究
作者：黃仁鵬 引用關係
作者(外文)：Huang, Jen-peng
出版日期：2016
卷期：14:3
頁次：頁365-388
主題關鍵詞：資料探勘；資料串流；滑動視窗；連續性路徑；Data mining；Data stream；Sliding window；Consecutive traversal paths
原始連結：連回原系統網址new window
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  • 點閱點閱:5
隨著資訊科技的進步，交易、文件、日常處理的資料被電子化大量的累積，資料探勘的技術變得日益重要。其中，資料流探勘在資料探勘的領域中也扮演相當重要的角色，隨著各種新興應用的崛起，例如網路流量分析、網頁點選串流探勘、全球衛星定位系統路徑分析等，所處理的不再是靜態的資料，而是一連串即時且連續的動態資料流 (dynamic data stream)。本研究主要針對在資料串流中的滑動視窗模式(sliding window model)下探勘連續性路徑而提出了一個新的演算法DSCP (an efficient algorithms for mining consecutive traversal paths from data stream)。DSCP演算法的樹狀結構在探勘過程中容易維護並且不需對其樹狀結構的節點做大量的調整並配合本研究所提出的遮罩路徑過濾機制及連續路徑結合機制，而有效加快連續性路徑的探勘速度。
Due to the science and technology make a great progress, transactions, documents and data are transformed into electronic types, the large number of data has been accumulated. Therefore, data mining technology becomes more important than before in recent years. In data mining territory, data stream mining plays an important role. The data of new applications is not static any more. Net flow analysis, web-click mining and Global Position System are examples of applications. Instead of generating static data, they generate dynamic and real time data stream. In this paper we propose an algorithm DSCP (An Efficient Algorithms for Mining Consecutive Traversal Paths from Data Stream). It uses the concept of CTP, DSCP-tree structure, and sliding window model for mining consecutive traversal paths from data stream.
期刊論文
1.Jiang, N.、Gruenwald, L.(2006)。Research Issues in Data Stream Association Rule Mining。SIGMOD Record，35(1)，14-19。  new window
2.Chen, M.-S.、Park, J. S.、Yu, P. S.(1998)。Efficient Data Mining for Path Traversal Patterns。IEEE Transactions on Knowledge and Data Engineering，10(2)，209-221。  new window
3.Elayyadi, I.、Benbernou, S.、Ouziri, M.、Younas, M.(2014)。A tensor-based distributed discovery of missing association rules on the cloud。Future Generation Computer Systems，35，49-56。  new window
4.Gaber, M. M.(2005)。Mining data streams: A review。SIGMOD Record，34(2)，18-26。  new window
5.Ghanbari, E.、Beigy, H.(2015)。Incremental RotBoost algorithm: An application for spam filtering。Intelligent Data Analysis，19(2)，449-468。  new window
6.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，212，191-212。  new window
7.Kim, Y.、Park, D.、Kim, H.、Kim, U.(2012)。A sliding window-based false-negative approach for ubiquitous data stream analysis。International Journal of Communication Systems，25(6)，691-716。  new window
8.Lakshmanan, L. V. S.、Leung, C. K.-S.、Ng, R. T.(2003)。Efficient dynamicmining of constrained frequent sets。ACM TODS，28(4)，337-389。  new window
9.Li, H.-F.(2011)。MHUI-max: An efficient algorithm for discovering high-utility itemsets from data streams。Journal of Information Science，37(5)，532-545。  new window
10.Sahoo, J.、Das, A. K.、Goswami, A.(2015)。An efficient approach for mining association rules from high utility itemsets。Expert Systems with Applications: An International Journal，42(13)，5754-5778。  new window
11.Zeng, X.-Q.、Li, G.-Z.(2014)。Incremental partial least squares analysis of big streaming data。Pattern Recognition，47(11)，3726-3735。  new window
12.Zhang, X.、Deng, Z. H.(2015)。Mining summarization of high utility itemsets。Knowledge-Based Systems，84，67-77。  new window
13.Zhou, X.、Yen, N. Y.、Jin, Q.、Shih, T. K.(2013)。Enriching user search experience by mining social streams with heuristic stones and associative ripples。Multimedia Tools and Applications，63(1)，129-144。  new window
14.Xiao, Y. Q.、Dunham, M. H.(2001)。Efficient mining of traversal patterns。Data and Knowledge Eng，39(2)，191-214。  new window
15.Datar, M.、Gionis, A.、Indyk, P.、Motwani, R.(2002)。Maintaining stream statistics over sliding windows。SIAM Journal on Computing，31(6)，1794-1813。  new window
會議論文
1.Agrawal, R.、Srikant, R.(1995)。Mining Sequential Patterns。The Eleventh International Conference on Data Engineering，(會議日期: 1995/03/06-1995/03/10)，3-14。  new window
2.Srikant, R.、Agrawal, R.(1996)。Mining Sequential Patterns: Generalizations and Performance Improvements。Advances in Database Technology Edbt '96: 5th International Conference on Extending Database Technology，(會議日期: 1996/03/25-1996/03/29)，3-17。  new window
3.Huang, H.、Wu, X.、Relue, R.(2002)。Association analysis with one scan of databases。2002 IEEE International Conference on Data Miningv。IEEE。629-632。  new window
4.Zhu, Y.、Shasha, D.(2002)。StartStream: Statistical monitoring of thousands of data streams in real time。The 28th international conference on Very Large Data Bases，358-369。  new window
5.Chang, J. H.、Lee, W. S.(2003)。Finding recent frequent itemsets adaptively over online data streams。The 9th ACM SIGKDD international conference on Knowledge Discovery and Data Mining。ACM。487-492。  new window
6.Pei, J.、Han, J.、Mortazavi-Asl, B.、Zhu, H.(2000)。Mining access pattern efficiently from Web logs。The Pacific-Asia Conference on Knowledge Discovery and Data Mining。Springer Berlin Heidelberg。396-407。  new window
7.Cohen, E.、Strauss, M.(2003)。Maintaining time decaying stream aggregates。The 22th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems。ACM。223-233。  new window
8.Leung, K.-S.、Khan, Q. I.(2006)。DSTree: A tree structure for the mining of frequent sets from data streams。The 6th International Conference on Data Mining。IEEE。928-932。  new window
 
 
 
 
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