Clustering has been one of the most popular approaches used in gene expression data analysis. A clustering method is typically used to partition genes according to their similarity of expression under different conditions. However, it is often the case that some genes behave similarly only on a subset of conditions and their behavior is uncorrelated over the rest of the conditions. As tradition...

Soft Computing models play an important role in the field of recognition, classification, data prediction, etc in various application fields. Soft Computing models include fuzzy logic, neural, network, genetic algorithm, particle swarm optimization, Bacterial forging algotithm, classification and clustering, etc., the extraction of hidden information from large database is possible through the ...

Clustering is a widely used technique in data mining application for discovering patterns in underlying data. Most traditional clustering algorithms are limited in handling datasets that contain categorical attributes. However, datasets with categorical types of attributes are common in real life data mining problem. For these data sets, no inherent distance measure, like the Euclidean distance...

Article history: Received 26 January 2014 Received in revised form 10 March 2014 Accepted 12 March 2014 Available online 31 March 2014

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Data mining is a computational intelligence discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decision making. Clustering is a primary data description method in data mining which group’s most similar data. The data clustering is an impor...

This paper presents a two-phase clustering algorithm for machine-cell and partfamily formation in the design of cellular manufacturing systems. The proposed algorithm begins with the determination of initial cluster centers via a linear assignment method using the least similar group representatives in its first phase. A fuzzy C-means clustering method is followed in its second phase for part-f...

This paper analyzes the suitability of fuzzy clustering methods for the discovery of relevant document relationships, motivated by the need for enhanced relevancebased navigation of Web-accessible resources. The performance evaluation of a modified Fuzzy c-Means algorithm is carried out, and a comparison with a traditional hard clustering technique is presented. Clustering precision and recall ...

Clustering is an important research topic that has practical applications in many 5elds. It has been demonstrated that fuzzy clustering, using algorithms such as the fuzzy C-means (FCM), has clear advantages over crisp and probabilistic clustering methods. Like most clustering algorithms, however, FCM and its derivatives need the number of clusters in the given data set as one of their initiali...

The management and analysis of big data has been identified as one of the most important emerging needs in recent years. This is because of the sheer volume and increasing complexity of data being created or collected. Current clustering algorithms can not handle big data, and therefore, scalable solutions are necessary. Since fuzzy clustering algorithms have shown to outperform hard clustering...