Visual analytics in FCA-based clustering
April 21, 2015 Β· Declared Dead Β· π International Joint Conference on the Analysis of Images, Social Networks and Texts
"No code URL or promise found in abstract"
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Authors
Yury Kashnitsky
arXiv ID
1504.05469
Category
cs.IR: Information Retrieval
Cross-listed
cs.AI
Citations
0
Venue
International Joint Conference on the Analysis of Images, Social Networks and Texts
Last Checked
4 months ago
Abstract
Visual analytics is a subdomain of data analysis which combines both human and machine analytical abilities and is applied mostly in decision-making and data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was developed to detect groups of objects with similar properties under similar conditions. It is used in Social Network Analysis (SNA) and is a basis for certain types of recommender systems. The problem of triclustering algorithms is that they do not always produce meaningful clusters. This article describes a specific triclustering algorithm and a prototype of a visual analytics platform for working with obtained clusters. This tool is designed as a testing frameworkis and is intended to help an analyst to grasp the results of triclustering and recommender algorithms, and to make decisions on meaningfulness of certain triclusters and recommendations.
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