Delta-MOCK algorithm for Evolutionary Multiobjective Clustering
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README.md

README.md

Delta-MOCK

Delta-MOCK is a new version of the multiobjective clustering with automatic k-determination (MOCK) algorithm. MOCK is an evolutionary approach to multiobjective data clustering, originally proposed by Julia Handl and Joshua Knowles [1]. Our new algorithm Delta-MOCK presents extensive changes and improves upon the effectiveness and computational efficiency of MOCK. This translates into a better scalability which is essential given the unprecedented volumes of data that require to be processed in current clustering applications.

Delta-MOCK is described in detail in our paper:

Mario Garza-Fabre, Julia Handl and Joshua Knowles. 
An Improved and More Scalable Evolutionary Approach to Multiobjective Clustering.
IEEE Transactions on Evolutionary Computation.
https://doi.org/10.1109/TEVC.2017.2726341

The source code of the implementation of Delta-MOCK studied in our paper, as well as our collection of test data sets, is made available through this repository.


Contact:

Mario Garza-Fabre - garzafabre@gmail.com
Julia Handl - julia.handl@manchester.ac.uk
Joshua Knowles - j.knowles@cs.bham.ac.uk

References:

1.	Julia Handl and Joshua Knowles. An Evolutionary Approach to Multiobjective Clustering, 
	IEEE Transactions on Evolutionary Computation, vol. 11, no. 1, pp. 56–76, 2007.