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 . 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.
Mario Garza-Fabre - firstname.lastname@example.org Julia Handl - email@example.com Joshua Knowles - firstname.lastname@example.org
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.