Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Mieczyslaw A. Klopotek"'
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:
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
Publikováno v:
Fundamenta Informaticae. 139:229-248
Latency of user-based and item-based recommenders is evaluated. The two algorithms can deliver high quality predictions in dynamically changing environments. However, their response time depends not only on the size, but also on the structure of unde
Publikováno v:
Advances in Intelligent and Soft Computing ISBN: 9783642231711
The purpose of this paper is to describe a new methodology dedicated to the analysis of boolean recommenders. The aim of most recommender system is to suggest interesting items to a given user. The most common criteria utilized to evaluate a system a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4bb7584e6f028c0d8bde9afa9ebc8815
https://doi.org/10.1007/978-3-642-23172-8_28
https://doi.org/10.1007/978-3-642-23172-8_28
In this chapter the authors discuss an application of an immune-based algorithm for extraction and visualization of clusters structure in large collection of documents. Particularly a hierarchical, topic-sensitive approach is proposed; it appears to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4d137e8047f84b5c6bc6f17f9aa55dc0
https://doi.org/10.4018/978-1-60566-310-4.ch008
https://doi.org/10.4018/978-1-60566-310-4.ch008
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540681229
ISMIS
ISMIS
We investigate the impact of an initialization strategy on the quality of fuzzy-based clustering, applied to creation of maps of text document collection. In particular, we study the effectiveness of bootstrapping as compared to traditional "randomiz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::39b44411ed9064fd1e54b8eb45bfcff3
https://doi.org/10.1007/978-3-540-68123-6_31
https://doi.org/10.1007/978-3-540-68123-6_31
Publikováno v:
Studies in Computational Intelligence ISBN: 9783540768265
In this chapter we focus on some problems concerning application of an immune-based algorithm to extraction and visualization of cluster structure. Particularly a hierarchical, topic-sensitive approach is proposed; it appears to be a robust solution
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a5b94210fa6fded1856463f5e5549fa3
https://doi.org/10.1007/978-3-540-76827-2_15
https://doi.org/10.1007/978-3-540-76827-2_15
Autor:
Michał Dramiński, Mieczyslaw A. Klopotek, Krzysztof Ciesielski, Slawomir T. Wierzchon, Dariusz Czerski
Publikováno v:
Advances in Information Processing and Protection ISBN: 9780387731360
Advances in Information Processing and Protection
Advances in Information Processing and Protection
In this paper we present new approach to compression of inverted lists in indexes of information retrieval systems. The technique exploits contextual information obtained from a non-supervised clustering process run on the document collection. A subs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c85cf4e4900991cd9f1720355773df93
https://doi.org/10.1007/978-0-387-73137-7_6
https://doi.org/10.1007/978-0-387-73137-7_6
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540748243
IDA
IDA
We present a novel approach to the growing neural gas (GNG). based clustering of the high-dimensional text data. We enhance our Contextual GNG models (proposed previously to shift the majority of calculations to context-sensitive, local sub-graphs an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::febb89c781391016c98f1228c7e57332
https://doi.org/10.1007/978-3-540-74825-0_26
https://doi.org/10.1007/978-3-540-74825-0_26
Publikováno v:
Discovery Science ISBN: 9783540464914
Discovery Science
Discovery Science
In this paper, we focus on the class of graph-based clustering models, such as growing neural gas or idiotypic nets for the purpose of high-dimensional text data clustering. We present a novel approach, which does not require operation on the complex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ca80c34b247809cee5292265659c16e
https://doi.org/10.1007/11893318_10
https://doi.org/10.1007/11893318_10