Browsing by Author "Li, HF"

Now showing 1 - 7 of 7

DSM-TKP: Mining Top-K Path traversal patterns over Web click-streams
(2005)
Li, HF
;
Lee, SY
;
Shan, MK
;
資訊工程學系
;
Department of Computer Science
Online, single-pass mining Web click streams poses some interesting computational issues, such as unbounded length of streaming data, possibly very fast arrival rate, and just one scan over previously arrived click-sequences. In this paper, we propose a new, single-pass algorithm, called DSM-TKP (Data Stream Mining for Top-K Path traversal patterns), for mining top-k path traversal patterns, where k is the desired number of path traversal patterns to be mined. An effective summary data structure called TKP-forest (Top-K Path forest) is used to maintain the essential information about the top-k path traversal patterns of the click-stream so far. Experimental studies show that DSM-TKP algorithm uses stable memory usage and makes only one pass over the streaming data.
Mining frequent closed structures in stremying melody sequences
(2004)
Li, HF
;
Lee, SY
;
Shan, MK
;
資訊工程學系
;
Department of Computer Science
In this paper, we study the problem of mining frequent closed structures in a continuous, infinite-sized, and fast changing music melody stream. By modeling a music melody as a sequence of chord-sets, we propose an efficient algorithm FCS-stream (Frequent Closed Structures of streaming melody sequences) for incremental mining of frequent closed structures in one scan of the continuous stream of chord-set sequences. An extended prefix-tree structure called TCS-tree (Temporal Closed Structure tree) is developed for storing compact, essential information about the frequent closed structures of the stream. Results from our theoretical analysis and experimental studies with synthetic data show that algorithm FCS-strearn satisfies the main performance requirements, namely, single-pass, bounded memory, and real-time, for data stream mining.
Online mining (recently) maximal frequent itemsets over data streams
(2005)
Li, HF
;
Lee, SY
;
Shan, MK
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資訊工程學系
;
Department of Computer Science
A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid rate. Mining data streams is more difficult than mining static databases because the huge, high-speed and continuous characteristics of streaming data. In this paper, we propose a new one-pass algorithm called DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets), which mines the set of all maximal frequent itemsets in landmark windows over data streams. A new summary data structure called summary frequent itemset forest (abbreviated as SFI-forest) is developed for incremental maintaining the essential information about maximal frequent itemsets embedded in the stream so far. Theoretical analysis and experimental studies show that the proposed algorithm is efficient and scalable for mining the set of all maximal frequent itemsets over the entire history of the data streams.
Online mining changes of items over continuous append-only and dynamic data streams
(2005)
Li, HF
;
Lee, SY
;
Shan, MK
;
資訊工程學系
;
Department of Computer Science
Online mining changes over data streams has been recognized to be an important task in data mining. Mining changes over data streams is both compelling and challenging. In this paper, we propose a new, single-pass algorithm, called MFC-append ((M) under bar ining (F) under bar requency (C) under bar hanges of append-only data streams), for discovering the frequent frequency-changed items, vibrated frequency changed items, and stable frequency changed items over continuous append-only data streams. A new summary data structure, called Change-Sketch, is developed to compute the frequency changes between two continuous data streams as fast as possible. Moreover, a MFC-append-based algorithm, called MFC-dynamic ((M) under bar ining (F) under bar requency (C) under bar hanges of dynamic data streams), is proposed to find the frequency changes over dynamic data streams. Theoretical analysis and experimental results show that our algorithms meet the major performance requirements, namely single-pass, bounded space requirement, and real-time computing, in mining data streams.
Online mining maximal frequent structures in continuous landmark melody streams
(2005-08-01)
Li, HF
;
Lee, SY
;
Shan, MK
;
資訊工程學系
;
Department of Computer Science
In this paper, we address the problem of online mining maximal frequent structures (Type I & II melody structures) in unbounded, continuous landmark melody streams. An efficient algorithm, called MMSLMS (Maximal Melody Structures of Landmark Melody Streams), is developed for online incremental mining of maximal frequent melody substructures in one scan of the continuous melody streams. In MMSLMS, a space-efficient scheme, called CMB (Chord-set Memory Border), is proposed to constrain the upper-bound of space requirement of maximal frequent melody structures in such a streaming environment. Theoretical analysis and experimental study show that our algorithm is efficient and scalable for mining the set of all maximal melody structures in a landmark melody stream. (c) 2005 Elsevier B.V. All rights reserved.
PNP: Mining of profile navigational patterns
(2002)
Li, HF
;
Shan, MK
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資訊工程學系
;
Department of Computer Science
Web usage mining is a key knowledge discovery research and as such has been well researched. So far, this research has focused mainly on databases containing access log data only. However, many real-world databases contain users profile data and current solutions for this situation are still insufficient. In this paper we have a large database containing of user profile information together with users web-pages navigational patterns. The user profile data includes quantitative attributes, such as salary or age, and categorical attributes, such as sex or marital status. We introduce the concept of profile navigation patterns, which discusses the problem of relating user profile information to navigation behavior. An example of such profile navigation pattern might be "20% of married people between age 25 and 30 have the similar navigational behavior <(ac)(c,b)(b,e)(e,a)(od)> ", where a, b, c, d, e are web pages in a web site. The navigation sequences may contain the generic traversal behavior, e.g. trend to backward moves, cycles etc. The objective of mining profile navigation patterns is to identify browser profile for web personalization. We present PNP, a new algorithm that discovers these profile navigation patterns. Scale-up experiments show that PNP scales linearly with the number of transactions.
Single-pass algorithms for mining frequency change patterns with limited space in evolving append-only and dynamic transaction data streams
(2004)
Li, HF
;
Lee, SY
;
資訊工程學系
;
Department of Computer Science
In this paper, we propose an online single-pass algorithm MFC-append (Mining Frequency Change patterns in append-only data streams) for online mining frequent frequency change items in continuous append-only data streams. An online space-efficient data structure called Change-Sketch is developed for providing fast response time to compute dynamic frequency changes between data streams. A modified approach MFC-dynamic (Mining frequency Change patterns in dynamic data streams) is also presented to mine frequency changes in dynamic data streams. The theoretic analyses show that our algorithms meet the major performance requirements of single-pass, bounded storage, and real time for streaming data mining.