An improved and more scalable evolutionary approach to multiobjective clustering

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An improved and more scalable evolutionary approach to multiobjective clustering. / Garza-Fabre, Mario; Handl, Julia; Knowles, Joshua.

In: IEEE Transactions on Evolutionary Computation, Vol. PP, No. 99, 09.08.2017.

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@article{03a7dbba56ed4b1f9709c977d0487e43,
title = "An improved and more scalable evolutionary approach to multiobjective clustering",
abstract = "The multiobjective realisation of the data clustering problem has shown great promise in recent years, yielding clear conceptual advantages over the more conventional, singleobjective approach. Evolutionary algorithms have largely contributed to the development of this increasingly active research area on multiobjective clustering. Nevertheless, the unprecedented volumes of data seen widely today pose significant challenges and highlight the need for more effective and scalable tools for exploratory data analysis. This paper proposes an improved version of the multiobjective clustering with automatic k-determination algorithm. Our new algorithm improves its predecessor in several respects, but the key changes are related to the use of an efficient, specialised initialisation routine and two alternative reduced-length representations. These design components exploit information from the minimum spanning tree and redefine the problem in terms of the most relevant subset of its edges. Our study reveals that both the new initialisation routine and the new solution representations not only contribute to decrease thecomputational overhead, but also entail a significant reduction of the search space, enhancing therefore the convergence capabilities and overall effectiveness of the method. These results suggest that the new algorithm proposed here will offer significant advantages in the realm of {\textquoteleft}big data{\textquoteright} analytics and applications.",
keywords = "evolutionary computation , data analysis , clustering methods , data mining , pareto optimization",
author = "Mario Garza-Fabre and Julia Handl and Joshua Knowles",
year = "2017",
month = aug,
day = "9",
doi = "10.1109/TEVC.2017.2726341",
language = "English",
volume = "PP",
journal = "IEEE Transactions on Evolutionary Computation",
issn = "1089-778X",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
number = "99",

}

RIS

TY - JOUR

T1 - An improved and more scalable evolutionary approach to multiobjective clustering

AU - Garza-Fabre, Mario

AU - Handl, Julia

AU - Knowles, Joshua

PY - 2017/8/9

Y1 - 2017/8/9

N2 - The multiobjective realisation of the data clustering problem has shown great promise in recent years, yielding clear conceptual advantages over the more conventional, singleobjective approach. Evolutionary algorithms have largely contributed to the development of this increasingly active research area on multiobjective clustering. Nevertheless, the unprecedented volumes of data seen widely today pose significant challenges and highlight the need for more effective and scalable tools for exploratory data analysis. This paper proposes an improved version of the multiobjective clustering with automatic k-determination algorithm. Our new algorithm improves its predecessor in several respects, but the key changes are related to the use of an efficient, specialised initialisation routine and two alternative reduced-length representations. These design components exploit information from the minimum spanning tree and redefine the problem in terms of the most relevant subset of its edges. Our study reveals that both the new initialisation routine and the new solution representations not only contribute to decrease thecomputational overhead, but also entail a significant reduction of the search space, enhancing therefore the convergence capabilities and overall effectiveness of the method. These results suggest that the new algorithm proposed here will offer significant advantages in the realm of ‘big data’ analytics and applications.

AB - The multiobjective realisation of the data clustering problem has shown great promise in recent years, yielding clear conceptual advantages over the more conventional, singleobjective approach. Evolutionary algorithms have largely contributed to the development of this increasingly active research area on multiobjective clustering. Nevertheless, the unprecedented volumes of data seen widely today pose significant challenges and highlight the need for more effective and scalable tools for exploratory data analysis. This paper proposes an improved version of the multiobjective clustering with automatic k-determination algorithm. Our new algorithm improves its predecessor in several respects, but the key changes are related to the use of an efficient, specialised initialisation routine and two alternative reduced-length representations. These design components exploit information from the minimum spanning tree and redefine the problem in terms of the most relevant subset of its edges. Our study reveals that both the new initialisation routine and the new solution representations not only contribute to decrease thecomputational overhead, but also entail a significant reduction of the search space, enhancing therefore the convergence capabilities and overall effectiveness of the method. These results suggest that the new algorithm proposed here will offer significant advantages in the realm of ‘big data’ analytics and applications.

KW - evolutionary computation

KW - data analysis

KW - clustering methods

KW - data mining

KW - pareto optimization

U2 - 10.1109/TEVC.2017.2726341

DO - 10.1109/TEVC.2017.2726341

M3 - Article

VL - PP

JO - IEEE Transactions on Evolutionary Computation

JF - IEEE Transactions on Evolutionary Computation

SN - 1089-778X

IS - 99

ER -