Extraction of Protein Sequence Motif Information using PSO K-Means

The main objective of the paper is to find the motif information.The functionalities of the proteins are ideally found from their motif information which is extracted using various techniques like clustering with k-means, hybrid k-means, self-organising maps, etc., in the literature. In this work protein sequence information is extracted using optimised k-means algorithm. The particle swarm optimisation technique is one of the frequently used optimisation method. In the current work the PSO k-means is used for motif information extraction. This paper also deals with the comparison between the motif information obtained from clusters and biclustersusing PSO k-means algorithm. The motif information acquired is based on the structure homogeneity of the protein sequence.

Evolutionary Biclustering of Clickstream Data

Biclustering is a two way clustering approach involving simultaneous clustering along two dimensions of the data matrix. Finding biclusters of web objects (i.e. web users and web pages) is an emerging topic in the context of web usage mining. It overcomes the problem associated with traditional clustering methods by allowing automatic discovery of browsing pattern based on a subset of attributes. A coherent bicluster of clickstream data is a local browsing pattern such that users in bicluster exhibit correlated browsing pattern through a subset of pages of a web site. This paper proposed a new application of biclustering to web data using a combination of heuristics and meta-heuristics such as K-means, Greedy Search Procedure and Genetic Algorithms to identify the coherent browsing pattern. Experiment is conducted on the benchmark clickstream msnbc dataset from UCI repository. Results demonstrate the efficiency and beneficial outcome of the proposed method by correlating the users and pages of a web site in high degree.This approach shows excellent performance at finding high degree of overlapped coherent biclusters from web data.