Paper: 349393
Title: Topological Organization for Categorical Data Clustering

-------------------- review 1 --------------------
---------------------------- REVIEW 1 --------------------------
PAPER: 13
TITLE: Topological Organization for Categorical Data Clustering
 
OVERALL RATING: 3 (strong accept) 
REVIEWER'S CONFIDENCE: 2 (medium) 
Relevance to this conference: 5 (excellent)
Originality/Uniqueness: 4 (good)
English readability: 5 (excellent)
Paper organization/presentation: 5 (excellent)
Has good survey been done?: 5 (excellent)


In the paper the Authors address very interesting and important problem from pattern recognition domain – categorical data clasterization and visualization. The Authors are undoubtedly very well prepared to process such a research – they prove it in the first section that contains state of art and current methods comparison. The Relational Analysis method they had developed and introduced in the paper is valuable and significant, yet more interesting because of using heuristic approach. The whole presentation is constructed understandably with great attention to details and the performance results are attached as well. It was very honest to remark possible drawbacks of the new approach.
The paper is prepared correctly with proper information structure, English readability is very good. 



-------------------- review 2 --------------------
---------------------------- REVIEW 2 --------------------------
PAPER: 13
TITLE: Topological Organization for Categorical Data Clustering
 
OVERALL RATING: 2 (accept) 
REVIEWER'S CONFIDENCE: 2 (medium) 
Relevance to this conference: 4 (good)
Originality/Uniqueness: 4 (good)
English readability: 4 (good)
Paper organization/presentation: 3 (fair)
Has good survey been done?: 3 (fair)


I am just wondering if the problem can be solved equally by using k-means; and if yes, whether the proposed method is really better, in what sense.