Browsing by Author "Wang, Ching-Yao"

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An incremental mining algorithm for maintaining sequential patterns using pre-large sequences
(2011-06-01)
Hong, Tzung-Pei
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Wang, Ching-Yao
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Tseng, Shian-Shyong
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資訊工程學系
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Department of Computer Science
Mining useful information and helpful knowledge from large databases has evolved into an important research area in recent years. Among the classes of knowledge derived, finding sequential patterns in temporal transaction databases is very important since it can help model customer behavior. In the past, researchers usually assumed databases were static to simplify data-mining problems. In real-world applications, new transactions may be added into databases frequently. Designing an efficient and effective mining algorithm that can maintain sequential patterns as a database grows is thus important. In this paper, we propose a novel incremental mining algorithm for maintaining sequential patterns based on the concept of pre-large sequences to reduce the need for rescanning original databases. Pre-large sequences are defined by a lower support threshold and an upper support threshold that act as gaps to avoid the movements of sequences directly from large to small and vice versa. The proposed algorithm does not require rescanning original databases until the accumulative amount of newly added customer sequences exceeds a safety bound, which depends on database size. Thus, as databases grow larger, the numbers of new transactions allowed before database rescanning is required also grow. The proposed approach thus becomes increasingly efficient as databases grow. (C) 2010 Elsevier Ltd. All rights reserved.
METHOD FOR DETECTION OF MANUFACTURE DEFECTS
(2005-07-07)
Tseng, Shian-Shyong
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Chen, Wei-Chou
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Wang, Ching-Yao
A novel method for detection of manufacture defects is disclosed. The invented method is designed to discover root cause machine sets that cause defects in product manufactured in a process wherein a plurality of machines is involved. The method comprises obtaining manufacture data that relate to manufacture of products in the manufacture process, generating a candidate list of machine set by analyzing the manufacture data wherein the machine set includes machines relative to defects in the products, and identifying root cause machine sets from the list of machine set wherein the root cause machine sets include machines highly related to the defects.
Online generation of association rules under multi-dimensional consideration based on negative-border
(2007-01-01)
Wang, Ching-Yao
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Tseng, Shian-Shyong
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Hong, Tzung-Pei
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Chu, Yian-Shu
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資訊工程學系
Recently, some researchers have developed incremental and online mining approaches to maintain association rules without having to re-process the entire database whenever the database is updated or user specified thresholds are changed. However, they usually can not flexibly obtain association rules or patterns from portions of data, consider problems with different aspects, or provide online decision support for users. We earlier developed an online mining approach for generation of association rules under multidimensional consideration. The multidimensional online mining approach may, however, get loose upper-bound support of candidate itemsets and thus cause excessive I/O and computation costs. In this paper, we attempt to apply the concept of a negative border to enlarge the mining information in the multidimensional pattern relation to help get tighter upper-bound, and thus reduce the number of candidate itemsets to consider. Based oh the extended multidimensional pattern relation, a corresponding online mining approach called Negative-Border Online Mining (NOM) is proposed to efficiently and effectively utilize the information of negative itemset in the negative border. Experiments for heterogeneous datasets are also performed to show the effectiveness of the proposed approach.