Browsing by Author "Wu, Kuan-Ting"

Now showing 1 - 2 of 2

Collective flow dynamics across a bacterial carpet: Understanding the forces generated
(2014-11-17)
Hsiao, Yi-Teng
;
Wang, Jing-Hui
;
Wu, Kuan-Ting
;
Tsai, Jengjan
;
Chang, Cheng-Hung
Bacterial carpets consist of randomly anchored uni-polar-flagellated sodium-motive bacterial matrix are prepared by flow deposition. Collective flow dynamics across the bacterial carpets are probed with optical tweezers-microsphere assay. Around the center of a uniform bacterial cluster, collective forces that pull microsphere towards carpet surface are detected at a distance of 10 mu m away from carpets. At sodium-motive driving over a critical value, the force magnitudes increase abruptly, suggesting a threshold-like transition of hydrodynamic synchronization across bacterial carpet. The abrupt force increase is explained in term of bifurcation to phase synchronization in a noisy non-linearly coupled rotor array mediated by hydrodynamic interactions. (C) 2014 AIP Publishing LLC.
Pattern filtering and classification for market basket analysis with profit-based measures
(2012-05-01)
Chen, Mu-Chen
;
Chao, Chuang-Min
;
Wu, Kuan-Ting
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
Market basket analysis is one of the typical applications in mining association rules. The valuable information discovered from data mining can be used to support decision making. Generally, support and confidence (objective) measures are used to evaluate the interestingness of association rules. However, in some cases, by using these two measures, the discovered rules may be not profitable and not actionable (not interesting) to enterprises. Therefore, how to discover the patterns by considering both objective measures (e. g. probability) and subjective measures (e. g. profit) is a challenge in data mining, particularly in marketing applications. This paper focuses on pattern evaluation in the process of knowledge discovery by using the concept of profit mining. Data Envelopment Analysis is utilized to calculate the efficiency of discovered association rules with multiple objective and subjective measures. After evaluating the efficiency of association rules, they are categorized into two classes, relatively efficient (interesting) and relatively inefficient (uninteresting). To classify these two classes, Decision Tree (DT)-based classifier is built by using the attributes of association rules. The DT classifier can be used to find out the characteristics of interesting association rules, and to classify the unknown (new) association rules.