Enhanced Firefly-K-Means Clustering with Adaptive Mutation and Central Limit Theorem for Automatic Clustering of High-Dimensional Datasets

Enhanced Clustering Based on K-means Clustering Algorithm and Proposed Genetic Algorithm with K-means Clustering

-In this paper targeted a variety of techniques, tactics and distinctive areas of the studies that are useful and marked because the crucial discipline of information mining technologies. The overall purpose of the system of statistics mining is to extract beneficial facts from a large set of information and changing it right into a shape that is comprehensible for in addition use. Clustering i...

Adaptive K-Means Clustering

Clustering is used to organize data for efficient retrieval. One of the problems in clustering is the identification of clusters in given data. A popular technique for clustering is based on K-means such that the data is partitioned into K clusters. In this method, the number of clusters is predefined and the technique is highly dependent on the initial identification of elements that represent...

Automatic Scale Selection for Clustering of Correlated High-dimensional Datasets

Clustering algorithms usually have one or more parameters that control the scale at which the algorithm looks at the data. We study the problem of simultaneously selecting parameter values for multiple datasets (clustering instances), some of which are a priori known to have similar values. We propose two optimization problems related to this task. We show that one of them is NP-hard, and give ...

K - Means Clustering for Automatic Image Segmentation

A hybridized K-means clustering approach for high dimensional dataset

Due to incredible growth of high dimensional dataset, conventional data base querying methods are inadequate to extract useful information, so researchers nowadays is forced to develop new techniques to meet the raised requirements. Such large expression data gives rise to a number of new computational challenges not only due to the increase in number of data objects but also due to the increas...