Zobrazeno 1 - 10
of 27
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
Publikováno v:
CEC
In this paper we make a comparison of two NMF based techniques of dataset characterization: clustering and hulling. The characteristics of a dataset should be understood as describing the content of a data set through several characteristic represent
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030594909
ISMIS
ISMIS
Kleinberg introduced an axiomatic system for clustering functions. Out of three axioms, he proposed two (scale invariance and consistency) are concerned with data transformations that should produce the same clustering under the same clustering funct
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a275608e85a6f5d9ea574dc97de942b
https://doi.org/10.1007/978-3-030-59491-6_33
https://doi.org/10.1007/978-3-030-59491-6_33
Publikováno v:
Computer Information Systems and Industrial Management ISBN: 9783030476786
CISIM
CISIM
The paper investigates several notions of graph Laplacians and graph kernels from the perspective of understanding the graph clustering via the graph embedding into an Euclidean space. We propose hereby a unified view of spectral graph clustering and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b23f7745ad3297ef01437caa3f0e520
https://doi.org/10.1007/978-3-030-47679-3_40
https://doi.org/10.1007/978-3-030-47679-3_40
Publikováno v:
Artificial Intelligence and Soft Computing ISBN: 9783030615338
ICAISC (2)
ICAISC (2)
The paper studies the major reason for the contradictions in the Kleinberg’s axiomatic system for clustering [9]. We found that the so-called consistency axiom is the single source of problems because it creates new clusters instead of preserving t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe9eea4edea6cfeeb2ebe0bb7c4fde44
https://doi.org/10.1007/978-3-030-61534-5_18
https://doi.org/10.1007/978-3-030-61534-5_18
Publikováno v:
IFIP Advances in Information and Communication Technology ISBN: 9783030491604
AIAI (1)
AIAI (1)
We present a novel justification why k-means clusters should be (hyper)ball-shaped ones. We show that the clusters must be ball-shaped to attain motion-consistency. If clusters are ball-shaped, one can derive conditions under which two clusters attai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d825b5e5c328b68f7acc13a9112f10ce
https://doi.org/10.1007/978-3-030-49161-1_10
https://doi.org/10.1007/978-3-030-49161-1_10
Publikováno v:
Advanced Data Mining and Applications ISBN: 9783030352301
ADMA
ADMA
In a former paper [10] we simplified the proof of a theorem on personalized random walk that is fundamental to graph nodes clustering and generalized it to bipartite graphs for a specific case where the probability of random jump was proportional to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b7524ec08a04c1876c5fe5922ae6f9d
https://doi.org/10.1007/978-3-030-35231-8_17
https://doi.org/10.1007/978-3-030-35231-8_17
Publikováno v:
Machine Learning, Optimization, and Data Science ISBN: 9783030375980
LOD
LOD
With Kleinberg’s axiomatic system for clustering, a discussion has been initiated, what kind of properties clustering algorithms should have and have not. As Ackerman et al. pointed out, the static properties studied by Kleinberg and other are not
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b312a5fbc4e7686c88594906cbb8a96
https://doi.org/10.1007/978-3-030-37599-7_22
https://doi.org/10.1007/978-3-030-37599-7_22
Publikováno v:
Studies in Big Data ISBN: 9783319693071
While Chap. 2 presented the broadness of the spectrum of clustering methods, this chapter focusses on clustering of objects that are embedded in some metric space and gives an insight into the potential variations of clustering algorithms driven by p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d2905e7d5eb7d51a1159acb56a4a8c3
https://doi.org/10.1007/978-3-319-69308-8_3
https://doi.org/10.1007/978-3-319-69308-8_3
Publikováno v:
Studies in Big Data ISBN: 9783319693071
This chapter is devoted to actions to be performed in order to get maximum insights into the data by application of clustering algorithms. For data preprocessing stage, methods for choosing the appropriate set of features and algorithms for selection
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2431bc11d18deb581785cd9bf9333c77
https://doi.org/10.1007/978-3-319-69308-8_4
https://doi.org/10.1007/978-3-319-69308-8_4