學術出版;;Publications

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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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An association mining method for time series and its application in the stock prices of TFT-LCD industry
(2004)
Huang, CF
;
Chen, YC
;
Chen, AP
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
TFT-LCD is one of industries currently promoted by the "Two Trillion and Twin Star Industries Development Plan" in Taiwan. This study endeavors to find out the stock price associations between the suppliers and manufacturers in the value chain of the TFT-LCD industry by means of data mining techniques, and meanwhile, to improve the Apriori algorithm so that it can facilitate association mining of discrete data points in a time series. An efficient data mining method which consists of two phases is proposed. In the first phase, data are classified and preprocessed using the algorithm proposed by R. Agrawal et al. (1996), then Apriori algorithm is applied to extract the strong association rules. The second phase further improves the Apriori algorithm by breaking down the traditional limitation of relying on pattern matching of continuous data for disclosing stock market behavior. By mining the association rules from the discrete data points in a time series and testing the corresponding hypotheses, statistically significant outcomes can be obtained. The proposed data mining method was applied to some real time-series of the stock prices of companies in the supply chain of TFT-LCD industry in Taiwan. It is suggested that a positive correlation does not necessarily exist between the companies' stock prices in the supply chain of TFT-LCD industry. For instance the result shows that, if the stock price of Sintek Phonrotic Corp., a company in the up stream of the value chain, soars for more than 5% in a day, the stock price of Tatung, a company in the down stream of the same value chain, may not respond positively accordingly. If an investor can short the stock of Tatung on the 7th day and long it back on the 10th day after Sintek's stock price soaring for more than 5%, the annual return of investment is 199.88% with 95% confidence interval. In conclusion, the results may reveal helpful information for the investors to make leveraged arbitrage profit investing decisions, and it might be interesting to apply this proposed data mining method to the time series in other industries or problems and investigate the results further.
A three-phased online association rule mining approach for diverse mining requests
(2004)
Wang, CY
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Tseng, SS
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Hong, TP
;
Chu, YS
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資訊工程學系
In the past, most incremental mining and online mining algorithms considered finding the set of association rules or patterns consistent with the entire set of data inserted so far. Users can not easily obtain the results from their only interested portion of data. For providing ad-hoc, query-driven and online mining supports, we first propose a relation called multidimensional pattern relation to structurally and systematically store the context information and the mining information for later analysis. Each tuple in the relation comes from an inserted dataset in the database. This concept is similar to the construction of a data warehouse for OLAP. However, unlike the summarized information of fact attributes in a data warehouse, the mined patterns in the multidimensional pattern relation can not be directly aggregated to satisfy, users' mining requests. We then develop an online mining approach called Three-phased Online Association Rule Mining (TOARM) based on the proposed multidimensional pattern relation to support online generation of association rules under multidimensional considerations. Experiments for both homogeneous and heterogeneous datasets are made, with results showing the effectiveness of the proposed approach.
Multidimensional on-line mining
(2006)
Wang, CY
;
Hong, TP
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Tseng, SS
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資訊工程學系
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Department of Computer Science
In the past;, incremental mining approaches usually considered getting the newest; set; of knowledge consistent; with the entire set of data inserted so far. Users can not, however, use them to obtain rules or patterns only from their interesting portion of the data. In addition, these approaches only focused on finding frequent patterns in a specified part of a database. That is, although the data records are collected in under certain time, place and category, such contexts (circumstances) have been ignored in conventional mining algorithms. It will cause the lack of patterns or rules to help users solve problems at, different, aspects and with diverse considerations. In this paper, we thus attempt; to extend incremental mining to online decision support under multidimensional context considerations. We first propose the multidimensional pattern relation to structurally and systematically retain the additional context information and mining information for each inserted dataset into a database. We then develop an algorithm based on the proposed multidimensional pattern relation to correctly and efficiently fulfill diverse on-fine mining requests.
Using data mining technology to build an intelligent manufacturing system for semiconductor industry
(2006)
Chen, Ruey-Shun
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Chang, Chan-Chine
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資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
In this paper, we propose a flexible three-tier architecture for data mining in semiconductor manufacturing. The architecture allows a high-performance implementation of data mining techniques using association rule. The prototype is able to mine patterns from large data sets that reside in databases. There are benefits to use the data mining control system that is implemented by association rule in computer integrated manufacturing (CIM) environment. The result shows that the proposed intelligent manufacturing system can improve product yield rate and reduce cycle time in semiconductor manufacturing. It could find the best machines of process machine sets that affect processes. It also enhances wafer yield accuracy, reduces production costs and promotes the enterprise competitiveness.
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.

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🏛️國立陽明交通大學機構典藏

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