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 - [email protected]
Julia Handl - [email protected]
Joshua Knowles - [email protected]

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.