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NCRAST Talk on Clustering

This document describes a meta-learning approach to scalable clustering using multiple independent base classifiers. The algorithm divides data into disjoint subsets to train separate classifiers, then cross-references their results to correlate categories. Experimental results on DNA splice junction and protein coding region datasets show classification accuracy improves over a single classifier, demonstrating the method can scale to large data.

Research Poster

Knowledge discovery in databases (KDD) is a process which consists of multiple contiguous steps, done iteratively to find knowledge from very large datasets. The data-mining step of knowledge discovery is computationally time consuming , if we work with very large databases. Clustering is one form of unsupervised learning in machine learning techniques used for data mining. Here, a Meta learning approach is taken by me that is used to scale supervised learning, to scale unsupervised learning. This approach uses a number of clustering algorithms to be performed for the base classification systems and then it learns relationships among the base classification systems to achieve final predictions. The base classifiers can be executed on independent computers to perform not only processor scaling, but data scaling also.

manojitcv

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panagenda

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