%0 Journal Article %1 kuradaautomatic %A Kurada, Ramachandra Rao %A Kanadam, Karteeka Pavan %D 2016 %J Advanced Computational Intelligence: An International Journal (ACII) %K Jaya Multi algorithm automatic cluster clustering evolutionary indexes objective optimization validity %N 2 %P 35 - 42 %R 10.5121/acii.2016.3204 %T Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithm %U http://airccse.org/journal/acii/vol3.html %V 3 %X In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization stratagem. This evolutionary technique always aims to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.

@article{kuradaautomatic, abstract = {In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization stratagem. This evolutionary technique always aims to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks. }, added-at = {2020-07-08T13:16:50.000+0200}, author = {Kurada, Ramachandra Rao and Kanadam, Karteeka Pavan}, biburl = {https://www.bibsonomy.org/bibtex/253d7688363d8987ba809092951fb49d7/janakirob}, doi = {10.5121/acii.2016.3204}, interhash = {3cbae350cc92c47929aa6b54ba79e257}, intrahash = {53d7688363d8987ba809092951fb49d7}, journal = {Advanced Computational Intelligence: An International Journal (ACII)}, keywords = {Jaya Multi algorithm automatic cluster clustering evolutionary indexes objective optimization validity}, month = {April}, number = 2, pages = {35 - 42}, timestamp = {2020-07-08T13:16:50.000+0200}, title = {Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithm}, url = {http://airccse.org/journal/acii/vol3.html}, volume = 3, year = 2016 }