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The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach (EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a nomenclature that highlights some aspects that are very important in the context of evolutionary data clustering. The paper missions the clustering… (More)

Unsupervised classification called clustering is a process of organizing objects into groups whose members are similar in some way. Clustering of uncertain data objects is a challenge in spatial data bases. In this paper we use Probability Density Functions (PDF) to represent these uncertain data objects, and apply Uncertain K-Means algorithm to generate… (More)

- Ramachandra Rao Kurada, Pavan Kanadam, Ramachandra Rao
- 2015

Many clustering algorithms have been proposed, yet most of them require predefined number of clusters. Unfortunately, unavailable information regarding number of clusters is commonly happened in real-world data of different domains. This study is aimed to overcome the above stated problem by developing a generalized automatic clustering algorithmic… (More)

Real world problems are also classified to multi-objective optimization problems since they are tailored with more than one objective functions for which the optimization is advantageous simultaneously. The best possible outcome among these objective function is being optimized from the set of solutions rather than finding a single solution. One of the most… (More)

Machine learning for text classification is the underpinning of document cataloging, news filtering, document steering and exemplification. In text mining realm, effective feature selection is significant to make the learning task more accurate and competent. One of the traditional lazy text classifier k-Nearest Neighborhood (kNN) has a major pitfall in… (More)

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