Hierarchical clustering of speakers by their pronunciation patterns could be a useful technique for the discovery of accents and the relationships between accents and sociological variables. However it is first necessary to ensure that the clustering is not influenced by the physical characteristics of the speakers. In this study a number of approaches to agglomerative hierarchical clustering o...

User generated content from fora, weblogs and other social networks is a very fast growing data source in which different information extraction algorithms can provide a convenient data access. Hierarchical clustering algorithms are used to provide topics covered in this data on different levels of abstraction. During the last years, there has been some research using hierarchical fuzzy algorit...

Nowadays the content based image retrieval (CBIR) is becoming a source of exact and fast retrieval. CBIR presents challenges in indexing, accessing of image data and how end systems are evaluated. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. Many clustering and segmentation algorithms both suffer from the limitation of the number of clusters speci...

The article proposes an approach to the development of models for assessing resilience macroeconomic systems effects exogenous shocks with emphasis on study diagnostic classes. relevance chosen research topic is explained by fact that world economy takes place in context increasing globalization processes. In this economic environment, both positive and negative these processes are formed. Due ...

Enhancement Clustering of Cloud Datasets using Improved Agglomerative Technique Prof. Madhuri h Parekh Smt. J.J.Kundaliya Commerce College, Rajkot, Gujarat, India. Email: [email protected] ----------------------------------------------------------------------ABSTRACT------------------------------------------------------------Cloud computing is the latest technology that delivers computing...

INTRODUCTION Document clustering is an automatic grouping of text documents into clusters so that documents within a cluster have high similarity in comparison to one another, but are dissimilar to documents in other clusters. Unlike document classification (Wang, Zhou, and He, 2001), no labeled documents are provided in clustering; hence, clustering is also known as unsupervised learning. Hier...

In recent years, the study of community detection in social networks has received great attention. The hierarchical structure of the network leads to the emergence of the convergence to a locally optimal community structure. In this paper, we aim to avoid this local optimum in the introduced hybrid hierarchical method. To achieve this purpose, we present an objective function where we incorpora...