The present research proposes a paradigm for the clustering of data in which no prior knowledge about the number of clusters is required. Here shape based ...

Abstract. The present research proposes a paradigm for the clustering of data in which no prior knowledge about the number of clusters is required.

Here shape based similarity is used as an index of similarity for clustering. The paper exploits the pattern identification prowess of Hidden Markov Model (HMM) ...

This paper introduces a novel approach which uses a Hidden Markov Model (HMM) based Artificial Neural Networks (ANN) for prediction of systems that are non ...

In this paper a novel algorithm for shape based time series clustering is proposed. It can reduce the size of data, improve the efficiency and not reduce the ...

This paper presents a novel method based on K-means Clustering Algorithm. First, the intensity values are divided into several groups by clustering method ...

Oct 1, 2000 · A novel similarity measure, proposed for clustering data with arbitrary distribution shapes, is developed. Such a new measure of similarity ...

In this paper, we propose a new shape-based clustering algorithm named Fractional Order Shape-based k-cluster(FrOKShape), which defines a multi-variable shape- ...

A Shape-based Clustering for Time Series (SCTS) is proposed using a novel averaging method called Ranking Shape- based Template Matching Framework (RSTMF), ...

Smriti Srivastava , Saurabh Bhardwaj , J. R. P. Gupta: A Novel Clustering Approach Using Shape Based Similarity. ISI 2012: 17-27. manage site settings.