TY - GEN
T1 - A modularity-based measure for cluster selection from clustering hierarchies
AU - dos Anjos, Francisco de Assis Rodrigues
AU - Gertrudes, Jadson Castro
AU - Sander, Jörg
AU - Campello, Ricardo J.G.B.
N1 - Funding Information:
CNPq and CAPES (Brazil), NSERC (Canada).
Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2019.
PY - 2019
Y1 - 2019
N2 - Extracting a flat solution from a clustering hierarchy, as opposed to deriving it directly from data using a partitional clustering algorithm, is advantageous as it allows the hierarchical relationships between clusters and sub-clusters as well their stability across different hierarchical levels to be revealed before any decision on what clusters are more relevant is made. Traditionally, flat solutions are obtained by performing a global, horizontal cut through a clustering hierarchy (e.g. a dendrogram). This problem has gained special importance in the context of density-based hierarchical algorithms, because only sophisticated cutting strategies, in particular non-horizontal local cuts, are able to select clusters at different density levels. In this paper, we propose an adaptation of a variant of the Modularity Q measure, widely used in the realm of community detection in complex networks, so that it can be applied as an optimization criterion to the problem of optimal local cuts through clustering hierarchies. Our results suggest that the proposed measure is a competitive alternative, especially for high-dimensional data.
AB - Extracting a flat solution from a clustering hierarchy, as opposed to deriving it directly from data using a partitional clustering algorithm, is advantageous as it allows the hierarchical relationships between clusters and sub-clusters as well their stability across different hierarchical levels to be revealed before any decision on what clusters are more relevant is made. Traditionally, flat solutions are obtained by performing a global, horizontal cut through a clustering hierarchy (e.g. a dendrogram). This problem has gained special importance in the context of density-based hierarchical algorithms, because only sophisticated cutting strategies, in particular non-horizontal local cuts, are able to select clusters at different density levels. In this paper, we propose an adaptation of a variant of the Modularity Q measure, widely used in the realm of community detection in complex networks, so that it can be applied as an optimization criterion to the problem of optimal local cuts through clustering hierarchies. Our results suggest that the proposed measure is a competitive alternative, especially for high-dimensional data.
KW - Cluster evaluation and selection
KW - Hierarchical clustering
UR - http://www.scopus.com/inward/record.url?scp=85063449317&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-6661-1_20
DO - 10.1007/978-981-13-6661-1_20
M3 - Article in proceedings
AN - SCOPUS:85063449317
SN - 9789811366604
T3 - Communications in Computer and Information Science
SP - 253
EP - 265
BT - Data Mining - 16th Australasian Conference, AusDM 2018, Revised Selected Papers
A2 - Zhao, Yanchang
A2 - Stirling, David
A2 - Koh, Yun Sing
A2 - Islam, Zahidul
A2 - Warwick, Graco
A2 - Li, Chang-Tsun
A2 - Islam, Rafiqul
PB - Springer
T2 - 16th Australasian Conference on Data Mining, AusDM 2018
Y2 - 28 November 2018 through 30 November 2018
ER -