M-Denclue for Effective Data Clustering in High Dimensional Non-Linear Data
R.Nandhakumar1, Antony Selvadoss Thanamani2

1R.Nandhakumar, Assistant Professor, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, Tamil Nadu, India.
2Dr. Antony Selvadoss Thanamani, Associate Professor & Head, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi, Tamil Nadu, India.

Manuscript received on October 17, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 2925-2927 | Volume-9 Issue-1, November 2019. | Retrieval Number: A9109119119/2019©BEIESP | DOI: 10.35940/ijitee.A9109.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Clustering is a data mining task devoted to the automatic grouping of data based on mutual similarity. Clustering in high-dimensional spaces is a recurrent problem in many domains. It affects time complexity, space complexity, scalability and accuracy of clustering methods. Highdimensional non-linear datausually live in different low dimensional subspaces hidden in the original space. As high‐dimensional objects appear almost alike, new approaches for clustering are required. This research has focused on developing Mathematical models, techniques and clustering algorithms specifically for high‐dimensional data. The innocent growth in the fields of communication and technology, there is tremendous growth in high dimensional data spaces. As the variant of dimensions on high dimensional non-linear data increases, many clustering techniques begin to suffer from the curse of dimensionality, de-grading the quality of the results. In high dimensional non-linear data, the data becomes very sparse and distance measures become increasingly meaningless. The principal challenge for clustering high dimensional data is to overcome the “curse of dimensionality”. This research work concentrates on devising an enhanced algorithm for clustering high dimensional non-linear data.
Keywords: Clustering, High Dimensional Non Linear Data, curse of dimensionality, Mathematical models.
Scope of the Article: Clustering