Abstract
The aim of this work is to propose a novel view on the well-known clustering approach that is here dealt with from a different perspective. We consider a kind of a reverse engineering related approach, which basically consists in discovering the broadly meant values of the parameters of the clustering algorithm, including the choice of the algorithm itself, or even – more generally – its class, and some other parameters, that have possibly led to a given partition of data, known a priori. We discuss the motivation and possible interpretations related to such a novel reversed process. In fact the main motivation is gaining insight into the structure of the given data set or even a family of data sets. The use of the evolutionary strategies is proposed to computationally implement such a reverse analysis. The idea and feasibility of the proposed computational approach is illustrated on two benchmark type data sets. The preliminary results obtained are promising in terms of a balance between analytic and computational effectiveness and efficiency, quality of results obtained and their comprehensiveness and intuitive appeal, a high application potential, as well as possibilities for further extensions.
To Saddaki, Professor Sadaaki Miyamoto, with a deep appreciation for his long time research and scholarly excellence and inspiration, and so many years of friendship
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Notes
- 1.
It is possible to start with a data similarity/distance matrix, if available, without an explicit characterization of the data in terms of some attributes values, and such a setting also seems to provide a reasonable context for the paradigm proposed in this paper, but we will leave this case for a possible further study.
- 2.
The concept of a Reverse Cluster Analysis has been introduced by Ríos and Velásquez [25] in case of the SOM based clustering but it is meant there in a rather different sense as associating original data points with the nodes in the trained network.
- 3.
Possibly except for an identifier attribute, which makes it possible to distinguish particular elements of X.
- 4.
Actually, even in the “absolute” case, doubts may arise, if the situation resembles the one of multiple overlapping distributions, i.e. although \(P_{A}\) is well established and “certain”, it is hardly reflected in the data, represented by X, so that many objects \(x_{i}\) might be equally well assigned to different clusters.
- 5.
Series 2: PC class 4-core processor station, 3.2 GHz, under 64 bit Linux Fedora 17; simulation software written in C and compiled with g++.
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Acknowledgements
Partially supported by the National Science Centre under Grant UMO-2012/05/B/ST6/03068.
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Owsiński, J.W., Kacprzyk, J., Opara, K., Stańczak, J., Zadrożny, S. (2017). Using a Reverse Engineering Type Paradigm in Clustering. An Evolutionary Programming Based Approach. In: Torra, V., Dahlbom, A., Narukawa, Y. (eds) Fuzzy Sets, Rough Sets, Multisets and Clustering. Studies in Computational Intelligence, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-319-47557-8_9
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