Browsing by Author "Ho, Chin-Chuan"

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A new algorithm for maintaining closed frequent itemsets in data streams by incremental updates
(2006)
Li, Hua-Fu
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Ho, Chin-Chuan
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Kuo, Fang-Fei
;
Lee, Suh-Yin
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資訊工程學系
Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed algorithm not only attain highly accurate mining results, but also run significant faster and consume less memory than existing algorithm Moment for mining closed frequent itemsets over recent data streams.
A SINGLE-SCAN ALGORITHM FOR MINING SEQUENTIAL PATTERNS FROM DATA STREAMS
(2012-03-01)
Li, Hua-Fu
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Ho, Chin-Chuan
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Chen, Hsuan-Sheng
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Lee, Suh-Yin
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資訊工程學系
Sequential pattern mining (SPAM) is one of the most interesting research issues of data mining. In this paper, a new research problem of mining data streams for sequential patterns is defined. A data stream is an unbound sequence of data elements arriving at a rapid rate. Based on the characteristics of data streams, the problem complexity of mining data streams for sequential patterns is more difficult than that of mining sequential patterns from large static databases. Therefore, mining sequential patterns from data streams is a challenging research issue of data mining and knowledge discovery. Hence, an efficient single-pass algorithm, called IncSparn (Incremental Sequential pattern mining of streaming itemset-sequences), is proposed for discovering sequential patterns from streaming itemset-sequences over extended sliding window models. In the framework of IncSpam algorithm, a new sliding window model, called CSW-BV (Customer Sliding Window with Bit-Vectors), and an extended lexicographic tree-based data structure, called LesSeq-Tree (Lexicographic Sequence Tree), are developed to reduce the time and memory needed to slide the windows over streaming data and maintain all sequential patterns of current sliding windows. Experimental results show that the proposed method is an efficient single-pass algorithm for mining sequential patterns from streaming data.
Incremental mining of sequential patterns over a stream sliding window
(2006)
Ho, Chin-Chuan
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Li, Hua-Fu
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Kuo, Fang-Fei
;
Lee, Suh-Yin
;
資訊工程學系
Incremental mining of sequential patterns from data streams is one of the most challenging problems in mining data streams. However, previous work of mining sequential patterns from data streams is almost focused on mining of patterns from stream of item-sequences, not stream of itemset-sequences. In this paper, we propose an efficient single-pass algorithm, called IncSPAM, to maintain the set of sequential patterns from itemset-sequence streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed IncSPAM algorithm is efficient for mining sequential patterns over data streams.
Incremental updates of closed frequent itemsets over continuous data streams
(2009-03-01)
Li, Hlia-Fu
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Ho, Chin-Chuan
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Lee, Suh-Yin
;
資訊工程學系
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Department of Computer Science
Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed algorithm not only attain highly accurate mining results. but also run significant faster and consume less memory than existing algorithm Moment for mining closed frequent itemsets over recent data streams (C) 2007 Elsevier Ltd. All rights reserved.
Online mining of recent music query streams
(2006)
Li, Hua-Fu
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Ho, Chin-Chuan
;
Shan, Man-Kwan
;
Lee, Suh-Yin
;
資訊工程學系
Mining multimedia data is one of the most important issues in data mining. In this paper, we propose an online one-pass algorithm to mine the set of frequent temporal patterns in online music query streams with a sliding window. An effective bit-sequence representation is used to reduce the processing time and memory needed to slide the windows. Experiments show that the proposed algorithm only needs a half of memory requirement of original music query data, and just scans the data once.