Fuzzy Data Mining Method Research Articles

Generalized Fuzzy Data Mining for Incomplete Information

Defining data with fuzziness made the knowledge discovery process easy and secure to data in data mining. The fuzzy data bases may have linguistic variables. In this paper, fuzzy conditional inference and reasoning are studied for generalized fuzzy data mining. Generalized fuzzy data mining and reasoning is studied with two membership functions “Belief” and “Disbelief”. The fuzzy logic with two membership functions will give more evidence than single membership function. The fuzzy certainty factor is studied as difference between these functions and made it as single membership function. The fuzzy data mining methods are studied. The generalized data mining is studied with different fuzzy conditional inferences. The business intelligence is given as an example.

MINING SIMPLIFIED FUZZY IF-THEN RULES FOR PATTERN CLASSIFICATION

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.

SIMPLE FUZZY GRID PARTITION FOR MINING MULTIPLE-LEVEL FUZZY SEQUENTIAL PATTERNS

Fuzzy sequential patterns are discovered by finding intertransaction fuzzy patterns among data items at a single level. In this paper, a fuzzy data mining method for finding fuzzy sequential patterns at multiple levels of abstraction is developed. Actually, a new method is proposed to mine multiple level fuzzy sequential patterns, using fuzzy partition by a simple fuzzy grid, among data items at a concept hierarchy. The proposed method is composed of two phases: one to find frequent level crossing fuzzy 1-sequences, and the other to generate multiple level fuzzy sequential patterns by analyzing the temporal relation between those frequent fuzzy 1-sequences. A numerical example along with a mining process is used to illustrate the usefulness of the proposed method.

A NEW FUZZY-DATA MINING METHOD FOR PATTERN CLASSIFICATION BY PRINCIPAL COMPONENT ANALYSIS

ABSTRACT Data mining techniques can be used to discover useful information by exploring and analyzing data. The aim of this article is to propose a new fuzzy-data mining method to find a compact set consisting of fuzzy if-then classification rules with high classification capability using the genetic algorithm. Furthermore, for not reducing the usefulness of the proposed method for classification problems with high dimensional feature space, the curse dimensionality resulting from the grid partition is overcome in the proposed method by employing the principal component analysis to reduce the dimensions. Through computer simulations, it can be seen that the proposed method is comparable to the other fuzzy classification methods on the well-known iris data, the appendicitis data, and the cancer data.

Finding useful fuzzy concepts for pattern classification using genetic algorithm

In this paper, a fuzzy classifier is treated as a fuzzy information retrieval system. To construct such a system, a fuzzy data mining method is employed to determine useful fuzzy concepts. Subsequently, each of the classes and patterns can be represented by a fuzzy set of useful fuzzy concepts. From the viewpoint of fuzzy information retrieval, a pattern can be categorized into one class if there exists a maximum degree of similarity between them. The genetic algorithm (GA), whose objective is to find a compact set consisting of useful fuzzy concepts with high classification capability, is further employed to automatically determine parameter specifications that are not easily specified by users. To evaluate classification performance of the proposed method, computer simulations are performed on some well-known classification problems, demonstrating that the generalization ability of the proposed method is comparable to other fuzzy classification methods.

Deriving two-stage learning sequences from knowledge in fuzzy sequential pattern mining

A fuzzy sequential pattern consisting of several fuzzy sets represents a frequently occurring behavior related to time and can be discovered from transaction bases. An example is that large purchase amounts of one product were bought by customers after these consumers had bought small purchase amounts of another product. Recently, Hu et al. (2003) proposed a fuzzy data mining method to discover fuzzy sequential patterns. In this method, consumers’ products preferences and consumers’ product buying orders related to purchase behaviors can be found in the fuzzy sequential pattern mining. Since for each decision problem, there is a competence set consisting of ideas, knowledge, information, and skills for solving that problem, we consider knowledge found in fuzzy sequential pattern mining as a needed competence set for solving one decision problem. This paper uses a known competence set expansion method, the minimum spanning table method, to find appropriate two-stage learning sequences that can effectively acquire individual fuzzy knowledge sets found in the fuzzy sequential pattern mining. A numerical example is used to show the usefulness of the proposed method.