For a long decade clustering faced a problem of noise and outliers. Support Vector Clustering is one of the techniques in pattern recognition. Support Vector Clustering is Kernel-Based Clustering. Division of patterns, data items, and feature vectors into groups (clusters) is a complicated task since clustering does not assume any prior knowledge, which are the clusters to be searched for. Nois...

Protein, RNA and DNA are made up of sequences of amino acids/nucleotides, which interact among themselves via binding. For example, (1) protein-DNA binding regulates gene transcription [1]; and (2) Protein-protein binding plays important roles in cell cycle control and signal transduction [2].The binding is maintained by either the direct participation or assistance of conserved short segments ...

artificial immune systems (ais) can be defined as soft computing systems inspired by immune system of vertebrates. immune system is an adaptive pattern recognition system. ais have been used in pattern recognition, machine learning, optimization and clustering. feature reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encoun...

ions, “Communications of the ACM, vol. 21, no. 5, pp. 401-410, 1978. [59] J. R. Quinlan, “Discovering rules by induction from large collections of examples” In D. Michie, editor, Expert Systems in the Micro-Electronic Age, pp. 168-210. Edinberg University

In an earlier companion paper [56] a supervised learning neural network pattern classifier called the fuzzy min-max classification neural network was described. In this sequel, the unsupervised learning pattern clustering sibling called the fuzzy min-max clustering neural network is presented. Pattern clusters are implemented here as fuzzy sets using a membership function with a hyperbox core t...

Pattern-based clustering is important in many applications, such as DNA micro-array data analysis, automatic recommendation systems and target marketing systems. However, pattern-based clustering in large databases is challenging. On the one hand, there can be a huge number of clusters and many of them can be redundant and thus make the pattern-based clustering ineffective. On the other hand, t...

The objective of the present paper is to describe a pattern recognition approach for image segmentation using fuzzy clustering. Soft computing techniques have found wide applications. One of the most important applications is edge detection for image segmentation. Clustering analysis is one of the major techniques in pattern recognition. These fuzzy clustering algorithms have been widely studie...

CURE Clustering divides the data sample into groups by identifying few representative points from each group of the data sample. This paper presents enhanced CURE as a clustering technique for data mining, in this approach we have a specially designed pattern as representative to form enhancement in CURE clustering to make it more usable efficiently on big data. Oracle 11G is used as backend wi...

While subspace clustering emerged as an application of pattern mining and some of its early advances have probably been inspired by developments in pattern mining, over the years both elds progressed rather independently. In this paper, we identify a number of recent developments in pattern mining that are likely to be applicable to alleviate or solve current problems in subspace clustering and...

While subspace clustering emerged as an application of pattern mining and some of its early advances have probably been inspired by developments in pattern mining, over the years both fields progressed rather independently. In this paper, we identify a number of recent developments in pattern mining that are likely to be applicable to alleviate or solve current problems in subspace clustering a...