Fuzzy Data Mining Approach Research Articles

Actionable high-coherent-utility fuzzy itemset mining

Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from quantitative transaction databases. Since each item has its own utility, utility itemset mining has become increasingly important. However, common problems with existing approaches are that an appropriate minimum support is difficult to determine and that the derived rules usually expose common-sense knowledge, which may not be interesting from a business point of view. This study thus proposes an algorithm for mining high-coherent-utility fuzzy itemsets to overcome problems with the properties of propositional logic. Quantitative transactions are first transformed into fuzzy sets. Then, the utility of each fuzzy itemset is calculated according to the given external utility table. If the value is larger than or equal to the minimum utility ratio, the itemset is considered as a high-utility fuzzy itemset. Finally, contingency tables are calculated and used for checking whether a high-utility fuzzy itemset satisfies four criteria. If so, it is a high-coherent-utility fuzzy itemset. Experiments on the foodmart and simulated datasets are made to show that the derived itemsets by the proposed algorithm not only can reach better profit than selling them separately, but also can provide fewer but more useful utility itemsets for decision-makers.

MOGA-based fuzzy data mining with taxonomy

Transactions in real-world applications usually consist of quantitative values. Some fuzzy data mining approaches have thus been proposed for deriving linguistic rules from such transactions. Since...

Evaluation of Distance Between Pedestrian Crossings by Students in One of the Polish Cities

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.

MOGA-based fuzzy data mining with taxonomy

Transactions in real-world applications usually consist of quantitative values. Some fuzzy data mining approaches have thus been proposed for deriving linguistic rules from such transactions. Since membership functions may have a critical influence on the final mining results, several genetic-fuzzy mining approaches have been proposed for mining appropriate membership functions and fuzzy association rules at the same time. Most of them, however, focus on a single level and consider only one objective function. This paper proposes a multi-objective multi-level genetic-fuzzy mining (MOMLGFM) algorithm for mining a set of non-dominated membership functions for mining multi-level fuzzy association rules. The algorithm first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. Two objective functions are then considered. The first one is the knowledge amount mined out at different levels, and the second one is the suitability of membership functions. The fitness value of each individual is then evaluated using these two objective functions. After the evolutionary process terminates, various sets of membership functions can be used for deriving multi-level fuzzy association rules according to decision-makers. Experimental results on the simulated and real datasets show the effectiveness of the proposed algorithm.

A fuzzy coherent rule mining algorithm

In real-world applications, transactions usually consist of quantitative values. Many fuzzy data mining approaches have thus been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, the common problems of those approaches are that an appropriate minimum support is hard to set, and the derived rules usually expose common-sense knowledge which may not be interesting in business point of view. In this paper, an algorithm for mining fuzzy coherent rules is proposed for overcoming those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy the four criteria or not. If yes, it is a fuzzy coherent rule. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm.

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

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.

Mining Fuzzy Association Patterns in Gene Expression Databases

In this paper, we propose two fuzzy data mining approaches for microarray analysis, namely Fuzzy Associative Gene Expression (FAGE) and Ripple Effective Gene Expression Rule (REGER) algorithms. Both of them first transform microarray data into fuzzy items, and then use fuzzy operators and specially-designed data structures to discover the relationships among genes. Through the proposed algorithms, a novel pattern named Ripple Pattern is discovered that indicates the genes active at the same time with their linguistic terms being monotone increasing or decreasing. The experimental results show that the proposed algorithms are effective in discovering novel and useful rules from microarray data.

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

ABSTRACT A fuzzy if-then rule whose consequent part is a real number is referred to as a simplified fuzzy rule. Since no defuzzification is required for this rule type, it has been widely used in function approximation problems. Furthermore, data mining can be used to discover useful information by exploring and analyzing data. Therefore, this paper proposes a fuzzy data mining approach to discover simplified fuzzy if-then rules from numerical data in order to approximate an unknown mapping from input to output. Since several pre-specified parameters for deriving fuzzy rules are not easily specified, they are automatically determined by the genetic algorithm with binary chromosomes. To evaluate performance of the proposed method, computer simulations are performed on various numerical data sets, showing that the fitting ability and the generalization ability of the proposed method are comparable to the known fuzzy rule-based methods.