FINDING SIMPLIFIED FUZZY IF-THEN RULES FOR FUNCTION APPROXIMATION PROBLEMS USING A FUZZY DATA MINING APPROACH

Abstract In this paper, the distances between pedestrian crossings in twenty one places in the city of Wrocław, together with their evaluation by the researched groups of students, were analyzed. The database created from the collected questionnaires contains a set of two-dimensional variables: the distance between crossings and the rating of the students. The database set was analyzed using a fuzzy data mining approach to create particular clusters. Various numbers of clusters were analyzed, and the division of data into three clusters made it possible to relate the analysis to the LOS methodology. Each variable was enriched with a third dimension representing a membership value. The obtained evaluated distances are similar to values recommended in literature, although the distances highly evaluated by the students do not often occur in reality. This might suggest that there is the need to create new crossings, especially in the city centre, where pedestrian trafic is or should be important.

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Precise and Accurate Job Cycle Time Forecasting in a Wafer Fabrication Factory with a Fuzzy Data Mining Approach

Mathematical Problems in Engineering ◽

10.1155/2013/496826 ◽

2013 ◽

Vol 2013 ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Toly Chen ◽

Richard Romanowski

Keyword(s):

Data Mining ◽

Cycle Time ◽

Principal Component ◽

Wafer Fabrication ◽

Fuzzy Data ◽

Lower And Upper Bounds ◽

Forecasting Accuracy ◽

Data Mining Approach ◽

Backpropagation Network ◽

Better Than

Many data mining methods have been proposed to improve the precision and accuracy of job cycle time forecasts for wafer fabrication factories. This study presents a fuzzy data mining approach based on an innovative fuzzy backpropagation network (FBPN) that determines the lower and upper bounds of the job cycle time. Forecasting accuracy is also significantly improved by a combination of principal component analysis (PCA), fuzzy c-means (FCM), and FBPN. An applied case that uses data collected from a wafer fabrication factory illustrates this fuzzy data mining approach. For this applied case, the proposed methodology performs better than six existing data mining approaches.

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MINING SIMPLIFIED FUZZY IF-THEN RULES FOR PATTERN CLASSIFICATION

International Journal of Information Technology & Decision Making ◽

10.1142/s021962200900348x ◽

2009 ◽

Vol 08 (03) ◽

pp. 473-489 ◽

Cited By ~ 8

Author(s):

YI-CHUNG HU ◽

FANG-MEI TSENG

Keyword(s):

Genetic Algorithm ◽

Function Approximation ◽

Fuzzy Rule ◽

Classification Performance ◽

The Other ◽

Training Data ◽

Fuzzy Data ◽

Mining Method ◽

Rule Based ◽

Approximation Problems

A fuzzy if-then rule whose consequent part is a real number is referred to as a simplified fuzzy rule. Simplified fuzzy if-then rules have been widely used in function approximation problems due to no complicated defuzzification is required. The proposed simplified fuzzy rule-based classification system, whose number of output is equal to the number of different classes, approximates an unknown mapping from input to desired output for each discriminant function. Not only a fuzzy data mining method is proposed to find simplified fuzzy if-then rules from training data, but also the genetic algorithm is employed to determine some user-specified parameters. To evaluate the classification performance of the proposed method, computer simulations are performed on some well-known datasets, showing that the generalization ability of the proposed method is comparable to the other fuzzy or nonfuzzy methods.

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