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pro vyhledávání: '"Mieczyslaw A. Klopotek"'
Autor:
Mieczyslaw A. Klopotek
Publikováno v:
Fundamenta Informaticae. 172:361-377
We prove in this paper that the expected value of the objective function of the $k$-means++ algorithm for samples converges to population expected value. As $k$-means++, for samples, provides with constant factor approximation for $k$-means objective
Autor:
Mieczyslaw A. Klopotek
We define the notion of a well-clusterable data set combining the point of view of the objective of $k$-means clustering algorithm (minimising the centric spread of data elements) and common sense (clusters shall be separated by gaps). We identify co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::288fe38bb79237de83897cd726a29bcb
http://arxiv.org/abs/1704.07139
http://arxiv.org/abs/1704.07139
Autor:
Mieczyslaw A. Klopotek
In this paper we would like to contest the results of Y. F. Wang, N. Karandikar and J. K. Aggarwal [ Pattern Recognition 24 , 1065–1085 (1991)] raising two fundamental claims. • • A line does not contribute anything to recognition of motion par
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::acd42b207dc556338dc6434cde9d6b72
http://arxiv.org/abs/1704.05267
http://arxiv.org/abs/1704.05267
Autor:
Mieczyslaw A. Klopotek
This paper investigates the application of consensus clustering and meta-clustering to the set of all possible partitions of a data set. We show that when using a "complement" of Rand Index as a measure of cluster similarity, the total-separation par
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::772c2207d9b005d2228d704215629928
Autor:
Mieczyslaw A. Klopotek
Hidden variables are well known sources of disturbance when recovering belief networks from data based only on measurable variables. Hence models assuming existence of hidden variables are under development. This paper presents a new algorithm "accel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::241270184377c85e5b4996d528b0174e
Publikováno v:
Annals of computer science and information systems, Vol 8, Pp 533-540 (2016)
FedCSIS
FedCSIS
Pattern-based methods of IS-A relation extraction rely heavily on so called Hearst patterns. These are ways of expressing instance enumerations of a class in natural language. While these lexico-syntactic patterns prove quite useful, they may not cap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92e2ebf3c218e29cb28b9af10a9d5c57
http://arxiv.org/abs/1605.02916
http://arxiv.org/abs/1605.02916