=Paper=
{{Paper
|id=Vol-1624/inv4
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1624/inv4.pdf
|volume=Vol-1624
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1624/inv4.pdf</pdf>
<pre>
Approximate Clusters, Biclusters and n-Clusters
  in the Analysis of Binary and General Data
                    Matrices

                                   Boris G. Mirkin

     National Research University Higher School of Economics, Moscow, Russia



       Abstract. Approximate cluster structures are those of formal concepts
       and n-concepts with added numerical intensity weights. The talk presents
       theoretical results and computational methods for approximate cluster-
       ing and n-clustering as extensions of the algebraic-geometrical properties
       of numerical matrices (SVD and the like) to the situations where one or
       most of elements of the solutions to be found are expressed by binary
       vectors. The theory embraces such methods as k-means, consensus clus-
       tering, network clustering, biclusters and triclusters and provides natural
       data analysis criteria, effective algorithms and interpretation tools.

       Keywords: Approximate clusters, biclusters, n-clusters, Formal Con-
       cept Analysis


References
1. Mirkin, B.G., Rostovtsev, P.S.: Method for revealing associated feature subsets,. In
   Mirkin, B., ed.: Models for Summarization of SocioEconomic Data (Metody Agre-
   girovania Sotsial’no-Economitcheskoi Informatsii), Novosibirsk: Institute of Eco-
   nomics Press (1978) 107–112 (in Russian).
2. Ignatov, D.I., Gnatyshak, D.V., Kuznetsov, S.O., Mirkin, B.G.: Triadic formal
   concept analysis and triclustering: searching for optimal patterns. Machine Learning
   101(1-3) (2015) 271–302

</pre>