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In this paper, a novel Feature-Reduction Fuzzy C-means (FRFCM) with Feature Linkage Weight (FRFCM-FLW) algorithm is introduced. By the combination of FRFCM and feature linkage weight, a new feature selection model is developed, called a Feature Linkage Weight Based FRFCM using fuzzy clustering. The larger amounts of features are superior to the complication of the problem, and the larger the time that is exhausted in creating the outcome of the classifier or the model. Feature selection has been established as a high-quality method for preferring features that best describes the data under certain criteria or measure. The proposed method presents three stages namely, 1) Data Formation: The process of data collection and data cleaning; 2) FRFCM-FLW. The proposed method can decrease feature elements routinely, and also construct excellent clustering results. The proposed method calculates a novel weight for every feature by combining modified Mahalanobis distance with feature δm variance in FRFCM algorithm; 3) Fuzzy C-means (FCM) cluster. The proposed FRFCM-FLW method proves high Accuracy Rate (AR), Rand Index (RI) and Jaccard Index (JI) ratio when compared to other feature reduction algorithms like WFCM, EWKM, WKM, FCM and FRFCM algorithms. [ABSTRACT FROM AUTHOR]

Recently, the topic of data mining has attracted considerable attention from both academia and industry. Data mining is the process of extracting useful knowledge from large amounts of data. Among the types of knowledge to be mined, classification knowledge is the most widely exploited in engineering applications. A variety of methods exist for inductive learning of "crisp" classification knowledge. This paper presents a new inductive learning algorithm called FuzzyRULES that extracts "fuzzy" classification rules from a database of examples. The use of fuzzy sets and fuzzy logic methods not only provides a powerful, flexible approach to handling vagueness and uncertainty, but also increases the expressive power and comprehensibility of the induced classification knowledge. An example involving the induction of process planning rules is used to illustrate the operation of FuzzyRULES. The algorithm has also been compared against conventional crisp and fuzzy rule induction algorithms on several benchmark data sets. The results obtained have shown that FuzzyRULES induces more compact and more accurate classification rule sets. [ABSTRACT FROM AUTHOR]