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Paper Details
Paper Title
INDECISIVE CONDITION CLASSIFICATION USING SVM
Authors
  Jyoti Pathak,  Sachin Patel
Abstract
In this research, we exploit the regularize framework and proposed an associative classification algorithm for uncertain data. The major recompense of SVM(support vector machine) are: recurrent item sets capture every dominant associations between items in a dataset. These classifiers naturally handle missing values and outliers as they only deal with statistically significant associations which build the classification to be vigorous. We proposed a novel indecisive SVM Based clustering algorithm which considers large databases as the major application. The SVM Based clustering algorithm will cluster a specified set of data and exploit the matching which proposes other works.
Keywords- support vector machine, indecisive data, associative classification, fuzzy clustering.
Publication Details
Unique Identification Number - IJEDR1401125Page Number(s) - 688-693Pubished in - Volume 2 | Issue 1 | March 2014DOI (Digital Object Identifier) -    Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
  Jyoti Pathak,  Sachin Patel,   "INDECISIVE CONDITION CLASSIFICATION USING SVM", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.2, Issue 1, pp.688-693, March 2014, Available at :http://www.ijedr.org/papers/IJEDR1401125.pdf
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