%0 Journal Article %1 syedquddusfederationuniversityaustralia2017algorithms %A Syed Quddus, Federation University Australia, Australia %D 2017 %E Jae-Kwang Lee, Brajesh Kumar Kaushik %J Computer Science & Information Technology (CS & IT) %K , Large-data Squared-error criterion, data mining,k-means sets. %N 2 %P 69-76 %T Fast Algorithms for Unsupervised Learning in Large Data Sets %U http://airccse.org/V7N64.html %V 7 %X The ability to mine and extract useful information automatically, from large datasets, is a common concern for organizations (having large datasets), over the last few decades. Over the internet, data is vastly increasing gradually and consequently the capacity to collect and store very large data is significantly increasing. Existing clustering algorithms are not always efficient and accurate in solving clustering problems for large datasets. However, the development of accurate and fast data classification algorithms for very large scale datasets is still a challenge. In this paper, various algorithms and techniques especially, approach using non-smooth optimization formulation of the clustering problem, are proposed for solving the minimum sum-of-squares clustering problems in very large datasets. This research also develops accurate and real time L2-DC algorithm based with the incremental approach to solve the minimum sum-of-squared clustering problems in very large datasets, in a reasonable time.

@article{syedquddusfederationuniversityaustralia2017algorithms, abstract = {The ability to mine and extract useful information automatically, from large datasets, is a common concern for organizations (having large datasets), over the last few decades. Over the internet, data is vastly increasing gradually and consequently the capacity to collect and store very large data is significantly increasing. Existing clustering algorithms are not always efficient and accurate in solving clustering problems for large datasets. However, the development of accurate and fast data classification algorithms for very large scale datasets is still a challenge. In this paper, various algorithms and techniques especially, approach using non-smooth optimization formulation of the clustering problem, are proposed for solving the minimum sum-of-squares clustering problems in very large datasets. This research also develops accurate and real time L2-DC algorithm based with the incremental approach to solve the minimum sum-of-squared clustering problems in very large datasets, in a reasonable time. }, added-at = {2017-02-20T01:21:27.000+0100}, author = {{Syed Quddus, Federation University Australia}, Australia}, biburl = {https://www.bibsonomy.org/bibtex/2bb0f59b12236032639d4dcde14928186/laimbee}, editor = {Jae-Kwang Lee, Brajesh Kumar Kaushik}, interhash = {a224df22b14d6efbed83cffc17fd0749}, intrahash = {bb0f59b12236032639d4dcde14928186}, journal = {Computer Science & Information Technology (CS & IT)}, keywords = {, Large-data Squared-error criterion, data mining,k-means sets.}, month = {January }, number = 2, pages = {69-76}, timestamp = {2017-02-20T01:21:27.000+0100}, title = {Fast Algorithms for Unsupervised Learning in Large Data Sets }, url = {http://airccse.org/V7N64.html}, volume = 7, year = 2017 }