M.M.Gowthul Alam

Most of the existing clustering approaches are applicable to purely numerical or categorical data only, but not the both. In general, it is a nontrivial task to perform clustering on mixed data composed of numerical and categorical attributes because there exists an awkward gap between the similarity metrics for categorical and numerical data. This paper therefore presents a general clustering framework based on the concept of objectcluster similarity and gives a unified similarity metric which can be simply applied to the data with categorical, numerical, and mixed attributes. This paper proposes a novel initialization method for mixed data which is implemented using K – Modes algorithm and further and iterative fuzzy K – Modes clustering algorithm. Keyword –Clustering, Similarity Metrics, Plasma compatability, Initialization,k-modes ,fuzzy kmodes,exemplers.

Grey wolf optimizer (GWO) is an efficient meta-heuristic algorithm that is inspired by the particular hunting behavior and leadership hierarchy of grey wolves in nature. In this paper, an efficient opposition-based grey wolf optimizer algorithm is proposed for solving the fuzzy clustering problem over artificial and real-life data. This work also tries to use the benefit of fuzzy properties which presents capability to handle overlapping clusters. However, centroid information and geometric structure information of clusters are the two important issues in fuzzy data clustering to improve the clustering performance. According to, in this paper, we derive two-objective functions, such as compactness and overlap–partition (OP) measures to handle above drawbacks. The centroid information issue is solved by compactness measure, and the OP measure is used to handle the geometric structure of clustering problem. Additionally, in the proposed clustering approach, the concept of opposition-b...

Clustering-based document retrieval system offers to find similar documents for a given user's query. This study explores the scope of kernel fuzzy c-means (KFCM) with the genetic algorithm on document retrieval issue. Initially, genetic algorithm-based kernel fuzzy c-means algorithm (GKFCM) is proposed to make the clustering of documents in the library. For each cluster, an index is created, which contains a common significant keywords of the documents for that cluster. Once the user enters the keyword as the input to the system, it will process the keywords with the WORDNET ontology to achieve the neighbourhood keywords and related synset keywords. Lastly, the documents inside the cluster are released at first as the resultant-related documents for the query keyword, which clusters have the maximum matching score values. Experiments results prove that GKFCM-based proposed system outperforms better performance than existing methods.