Initialization of Recursive Mixture-based Clustering with Uniform Components
Result description
The paper deals with a task of initialization of the recursive mixture estimation for the case of uniform components. This task is significant as a part of mixture-based clustering, where data clusters are described by the uniform distributions. The issue is extensively explored for normal components. However, sometimes the assumption of normality is not suitable or limits potential application areas (e.g., in the case of data with fixed bounds). The use of uniform components can be beneficial for these cases. Initialization is always a critical task of the mixture estimation. Within the considered recursive estimation algorithm the key point of its initialization is a choice of initial statistics of components. The paper explores several initialization approaches and compares results of clustering with a theoretical counterpart. Experiments with real data are demonstrated.
Keywords
Mixture-based ClusteringRecursive Mixture EstimationUniform Components
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Initialization of Recursive Mixture-based Clustering with Uniform Components
Original language description
The paper deals with a task of initialization of the recursive mixture estimation for the case of uniform components. This task is significant as a part of mixture-based clustering, where data clusters are described by the uniform distributions. The issue is extensively explored for normal components. However, sometimes the assumption of normality is not suitable or limits potential application areas (e.g., in the case of data with fixed bounds). The use of uniform components can be beneficial for these cases. Initialization is always a critical task of the mixture estimation. Within the considered recursive estimation algorithm the key point of its initialization is a choice of initial statistics of components. The paper explores several initialization approaches and compares results of clustering with a theoretical counterpart. Experiments with real data are demonstrated.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
GA15-03564S: Clustering and classification using recursive mixture estimation
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017)
ISBN
978-989-758-263-9
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
449-458
Publisher name
SCITEPRESS
Place of publication
Setúbal
Event location
Madrid
Event date
Jul 26, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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Basic information
Result type
D - Article in proceedings
OECD FORD
Statistics and probability
Year of implementation
2017