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https://www.researchgate.net/publication/220072750_Dynamic_Dissimilarity_Measure_for_Support-Based_Clustering
Dynamic Dissimilarity Measure for Support-Based Clustering Article in IEEE Transactions on Knowledge and Data Engineering 22(6):900-905 · June 2010 with 19 Reads How we measure 'reads'
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5072217
Abstract: Clustering methods utilizing support estimates of a data distribution have recently attracted much attention because of their ability to generate cluster boundaries of arbitrary shape and to deal with outliers efficiently. In this paper, we propose a novel dissimilarity measure based on a dynamical system associated with support estimating functions.
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000005072217
Clustering methods utilizing support estimates of a data distribution have recently attracted much attention because of their ability to generate cluster boundaries of arbitrary shape and to deal with outliers efficiently. In this paper, we propose a novel dissimilarity measure based on a dynamical system associated with support estimating functions. Theoretical foundations of the proposed ...Cited by: 77
https://core.ac.uk/display/45870118
In this paper, we propose a novel dissimilarity measure based on a dynamical system associated with support estimating functions. Theoretical foundations of the proposed measure are developed and applied to construct a clustering method that can effectively partition the whole data space.Author: D. Lee
https://www.jpinfotech.org/clustering-with-multi-viewpoint-based-similarity-measure/
Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. We compare them with several well-known clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal.
https://www.researchgate.net/publication/328895171_Clustering_time_series_based_on_dependence_structure
Nov 12, 2018 · Unlike model-based clustering methods, distance-based methods cluster time series in a simple and efficient way, where the choice of a proper distance or dissimilarity measure is a critical step.
https://www.sciencedirect.com/science/article/pii/S0360544219304074
May 01, 2019 · For the twelve clustering-based strategies which used a single dissimilarity measure, the raw time series data of the electricity usage per unit floor area of each building were segmented into 24-h DEUPs. The only difference among them is that they used different combinations of the clustering method and dissimilarity measure.Cited by: 1
https://link.springer.com/chapter/10.1007/978-3-319-03095-1_4
The majority clustering skill must presume some cluster relationship relating to the data set. Similarity among the items is usually defined sometimes clearly or even absolutely. With this paper, we introduced some sort of novel numerous reference centered similarity measure and …Author: Ch. S. Narayana, P. Ramesh Babu, M. Nagabushana Rao, Ch. Pramod Chaithanya
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4686108/
Dec 11, 2015 · Improving clustering performance has always been a target for researchers. Since in distance-based clustering similarity or dissimilarity (distance) measures are the core algorithm components, their efficiency directly influences the performance of clustering algorithms.Cited by: 104
https://www.sciencedirect.com/science/article/pii/S0031320311002640
2. Partitioning hard clustering algorithms based on multiple dissimilarity matrices. This section introduces partitioning dynamic hard clustering algorithm for relational data that are able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. 2.1.Cited by: 70
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