Improving classifications through graph embeddings

Anirban Chatterjee; Sanjukta Bhowmick; Padma Raghavan

doi:10.1007/978-1-4614-4457-2_5

Improving classifications through graph embeddings

Anirban Chatterjee, Sanjukta Bhowmick, Padma Raghavan

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Unsupervised classification is used to identify similar entities in a dataset and is extensively used in many application domains such as spam filtering [5], medical diagnosis [15], demographic research [13], etc. Unsupervised classification using K-Means generally clusters data based on (1) distance-based attributes of the dataset [4, 16, 17, 23] or (2) combinatorial properties of a weighted graph representation of the dataset [8].

Original language	English (US)
Title of host publication	Graph Embedding for Pattern Analysis
Publisher	Springer New York
Pages	119-138
Number of pages	20
ISBN (Electronic)	9781461444572
ISBN (Print)	9781461444565
DOIs	https://doi.org/10.1007/978-1-4614-4457-2_5
State	Published - Jan 1 2013

ASJC Scopus subject areas

Engineering(all)

Access to Document

10.1007/978-1-4614-4457-2_5

Cite this

Chatterjee, A., Bhowmick, S., & Raghavan, P. (2013). Improving classifications through graph embeddings. In Graph Embedding for Pattern Analysis (pp. 119-138). Springer New York. https://doi.org/10.1007/978-1-4614-4457-2_5

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