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The cluster analysis deals with the problems of organization of a collection of data objects into clusters based on similarity. It is also known as the unsupervised classification of objects and has found many applications in different areas. An important component of a clustering algorithm is the distance measure which is used to find the similarity between data objects. K-means is one of the most...
A major use of microarray data is to classify genes with similar expression profiles into groups in order to investigate their biological significance. Cluster analysis is by far the most used technique for gene expression analysis. It has grown to be an important research topic in a wide variety of fields owing to its wide applications. A number of clustering methods exist with one or more limitations,...
Distance computation is one of the most computationally intensive operations employed by many data mining algorithms. Performing such matrix computations within a DBMS creates many optimization challenges. We propose techniques to efficiently compute Euclidean distance using SQL queries and user-defined functions (UDFs). We concentrate on efficient Euclidean distance computation for the well-known...
Rival penalized competitive learning (RPCL) and its variants can perform clustering analysis efficiently with the ability of selecting the cluster number automatically. Although they have been widely applied in a variety of research areas, some of their problems have not yet been solved. Based on the semi-competitive learning mechanism of competitive and cooperative learning (CCL), this paper presents...
This paper describes a speaker-independent accent-based natural language call-routing system. Based on a speaker's accent group, this system directs customer calls to the automatic speech recognition system that is most suitable to recognize the input query. The speech recognition system understands the caller's query and converts it into routing keywords. Accent identification is the most important...
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