K. Karteeka Pavan

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1 Department of Computer Applications, Rayapati Venkata Ranga Rao and Jagarlamudi Chadramouli College of Engineering, Guntur, India 2 Jawaharlal Nehru Technological University, Kakinada, India 3 Department of Statistics, Acharya Nagarjuna University, Guntur, India, 4 Endocrine and Diabetes Centre, Andhra Pradesh, India kanadamkarteeka@gmail.com,(More)
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)
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find(More)
Heuristic evolutionary optimization algorithms are the solutions to many engineering optimization problems. Differential evolution (DE) is a real stochastic evolutionary parameter optimization in current use.DE does not require more control parameters compared to other evolutionary algorithms. Master Production Scheduling (MPS) is posed as one of multi(More)
Our proposed method is a two phase scheme that enhances the performance of K-means vector quantization algorithm for compressing images. In the proposed method, we have explored the possibility of application of statistical parameters for choosing the initial seeds for K-means algorithm. The selection of initial seeds depends on the statistical features of(More)
Determining optimal number of clusters in a dataset is a challenging task. Though some methods are available, there is no algorithm that produces unique clustering solution. The paper proposes an Automatic Merging for Single Optimal Solution (AMSOS) which aims to generate unique and nearly optimal clusters for the given datasets automatically. The AMSOS is(More)
Due to the diversity of documents on web, language identification is a vital task for web search engines during crawling and indexing of web documents. Among the current challenges in language-identification, the unsettled problem remains identifying Romanized text language. The challenge in Romanized text is the variations in word spellings and sounds in(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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