A Systematic Approach for Analyzing the Patient’s Future Diseases Using Incremental Semi Supervised Clustering

Paper ID: EIJTEM_2017_4_3_1-3

Author's Name: R. Anitha and M.R. Ramya

Volume: 4

Issue: 3

Year: 2017

Page No: 1-3

Abstract:

In many machine learning domains (e.g. Text processing, bioinformatics), there is a large supply of unlabeled data but limited labeled data, which can be expensive to generate. Consequently, semi-supervised learning, learning from a combination of both labeled and unlabeled data, has become a topic of significant recent interest.It Overcomes the three limitations of the Traditional cluster ensemble approaches: They do not make use of prior knowledge of the datasets given by experts. Most of the conventional cluster ensemble methods cannot obtain satisfactory results when handling high dimensional data. All the ensemble members are considered, even the ones without positive contributions. In this projects to compare the gene details. First include our normal data. Then include our diabetic patient data in our dataset. After that compare our data and to show result is positive or negative. The incremental ensemble member selection process is newly designed to judiciously remove redundant ensemble members based on a newly proposed local cost function and a global cost function, Finally, a set of nonparametric tests are adopted to compare multiple semi-supervised clustering ensemble approaches over different datasets to produce the satisfactory result

Keywords: Clustering Analysis, Diapetic Profile, Incremental Ensemble, Semi Supervised Clustering.

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