A New Homogeneity Inter-Clusters Measure in SemiSupervised Clustering

Many studies in data mining have proposed a new learning called semi-Supervised. Such type of learning combines unlabeled and labeled data which are hard to obtain. However, in unsupervised methods, the only unlabeled data are used. The problem of significance and the effectiveness of semi-supervised clustering results is becoming of main importance. This paper pursues the thesis that muchgreater accuracy can be achieved in such clustering by improving the similarity computing. Hence, we introduce a new approach of semisupervised clustering using an innovative new homogeneity measure of generated clusters. Our experimental results demonstrate significantly improved accuracy as a result.

• Publication year

  2013

• Venue

  arXiv.org

• Publication date

  2013-03-15

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.5120/11267-6526arXiv 1304.3840
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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