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RISS 인기검색어
- 검색키워드
- 저자 : Tanbeer, S.K. (검색결과 6 건)
- 무료
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Efficient single-pass frequent pattern mining using a prefix-tree
Tanbeer, S.K.,Ahmed, C.F.,Jeong, B.S.,Lee, Y.K. North-Holland [etc ; Elsevier Science Ltd 2009 Information sciences Vol.179 No.5
The FP-growth algorithm using the FP-tree has been widely studied for frequent pattern mining because it can dramatically improve performance compared to the candidate generation-and-test paradigm of Apriori. However, it still requires two database scans, which are not consistent with efficient data stream processing. In this paper, we present a novel tree structure, called CP-tree (compact pattern tree), that captures database information with one scan (insertion phase) and provides the same mining performance as the FP-growth method (restructuring phase). The CP-tree introduces the concept of dynamic tree restructuring to produce a highly compact frequency-descending tree structure at runtime. An efficient tree restructuring method, called the branch sorting method, that restructures a prefix-tree branch-by-branch, is also proposed in this paper. Moreover, the CP-tree provides full functionality for interactive and incremental mining. Extensive experimental results show that the CP-tree is efficient for frequent pattern mining, interactive, and incremental mining with a single database scan.
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Mining Regular Patterns in Transactional Databases
TANBEER, Syed Khairuzzaman,AHMED, Chowdhury Farhan,JEONG, Byeong-Soo,LEE, Young-Koo The Institute of Electronics, Information and Comm 2008 IEICE transactions on information and systems Vol.91 No.11
<P>The frequency of a pattern may not be a sufficient criterion for identifying meaningful patterns in a database. The temporal regularity of a pattern can be another key criterion for assessing the importance of a pattern in several applications. A pattern can be said <I>regular</I> if it appears at a regular user-defined interval in the database. Even though there have been some efforts to discover <I>periodic</I> patterns in time-series and sequential data, none of the existing studies have provided an appropriate method for discovering the patterns that occur regularly in a transactional database. Therefore, in this paper, we introduce a novel concept of mining <I>regular</I> patterns from transactional databases. We also devise an efficient tree-based data structure, called a Regular Pattern tree (RP-tree in short), that captures the database contents in a highly compact manner and enables a pattern growth-based mining technique to generate the complete set of <I>regular</I> patterns in a database for a user-defined <I>regularity</I> threshold. Our performance study shows that mining <I>regular</I> patterns with an RP-tree is time and memory efficient, as well as highly scalable.</P>
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I-Tree: A Frequent Patterns Mining Approach without Candidate Generation or Support Constraint
( Syed Khairuzzaman Tanbeer ),( Jehad Sarkar ),( Byeong-soo Jeong ),( Young-koo Lee ),( Sungyoung Lee ) 한국정보처리학회 2007 한국정보처리학회 학술대회논문집 Vol.14 No.1
Devising an efficient one-pass frequent pattern mining algorithm has been an issue in data mining research in recent past. Pattern growth algorithms like FP-Growth which are found more efficient than candidate generation and test algorithms still require two database scans. Moreover, FP-growth approach requires rebuilding the basetree while mining with different support counts. In this paper we propose an item-based tree, called I-Tree that not only efficiently mines frequent patterns with single database scan but also provides multiple mining scopes with multiple support thresholds. The ‘build-once-mine-many’ property of I-Tree allows it to construct the tree only once and perform mining operation several times with the variation of support count values.
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아메드파한,Syed Khairuzzaman Tanbeer,Byeong-Soo Jeong 한국전자통신연구원 2010 ETRI Journal Vol.32 No.5
Mining sequential patterns is an important research issue in data mining and knowledge discovery with broad applications. However, the existing sequential pattern mining approaches consider only binary frequency values of items in sequences and equal importance/significance values of distinct items. Therefore, they are not applicable to actually represent many real-world scenarios. In this paper, we propose a novel framework for mining high- utility sequential patterns for more real-life applicable information extraction from sequence databases with non-binary frequency values of items in sequences and different importance/significance values for distinct items. Moreover, for mining high-utility sequential patterns, we propose two new algorithms: UtilityLevel is a high-utility sequential pattern mining with a level-wise candidate generation approach, and UtilitySpan is a high-utility sequential pattern mining with a pattern growth approach. Extensive performance analyses show that our algorithms are very efficient and scalable for mining high-utility sequential patterns.
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Handling Dynamic Weights in Weighted Frequent Pattern Mining
AHMED, Chowdhury Farhan,TANBEER, Syed Khairuzzaman,JEONG, Byeong-Soo,LEE, Young-Koo The Institute of Electronics, Information and Comm 2008 IEICE transactions on information and systems Vol.91 No.11
<P>Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.</P>
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A framework for mining interesting high utility patterns with a strong frequency affinity
Ahmed, C.F.,Tanbeer, S.K.,Jeong, B.S.,Choi, H.J. North-Holland [etc ; Elsevier Science Ltd 2011 Information sciences Vol.181 No.21
High utility pattern (HUP) mining is one of the most important research issues in data mining. Although HUP mining extracts important knowledge from databases, it requires long calculations and multiple database scans. Therefore, HUP mining is often unsuitable for real-time data processing schemes such as data streams. Furthermore, many HUPs may be unimportant due to the poor correlations among the items inside of them. Hence,the fast discovery of fewer but more important HUPs would be very useful in many practical domains. In this paper, we propose a novel framework to introduce a very useful measure, called frequency affinity, among the items in a HUP and the concept of interesting HUP with a strong frequency affinity for the fast discovery of more applicable knowledge. Moreover, we propose a new tree structure, utility tree based on frequency affinity (UTFA), and a novel algorithm, high utility interesting pattern mining (HUIPM), for single-pass mining of HUIPs from a database. Our approach mines fewer but more valuable HUPs, significantly reduces the overall runtime of existing HUP mining algorithms and is applicable to real-time data processing. Extensive performance analyses show that the proposed HUIPM algorithm is very efficient and scalable for interesting HUP mining with a strong frequency affinity.
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