Clustering High Dimensional Non-Linear Data with Denclue, Optics and Clique Algorithms
R. Nandhakumar1, Antony Selvadoss Thanamani2
1R.Nandhakumar, Assistant Professor, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi-642001, India.
2Dr. Antony Selvadoss Thanamani, Associate Professor & Head, Department of Computer Science, Nallamuthu Gounder Mahalingam College, Pollachi-642001, India.
Manuscript received on 01 August 2019. | Revised Manuscript received on 06 August 2019. | Manuscript published on 30 September 2019. | PP: 8844-8848 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6671098319/2019©BEIESP | DOI: 10.35940/ijrte.C6671.098319
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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 technique in data mining which deals with huge amount of data. Clustering is intended to help a user in discovering and understanding the natural structure in a data set and abstract the meaning of large dataset. It is the task of partitioning objects of a data set into distinct groups such that two objects from one cluster are similar to each other, whereas two objects from distinct clusters are dissimilar. Clustering is unsupervised learning in which we are not provided with classes, where we can place the data objects. With the advent growth of high dimensional data such as microarray gene expression data, and grouping high dimensional data into clusters will encounter the similarity between the objects in the full dimensional space is often invalid because it contains different types of data. The process of grouping into high dimensional data into clusters is not accurate and perhaps not up to the level of expectation when the dimension of the dataset is high.
Keywords: Clustering, DNA Micro array, Noise, Density Based, Grid Based.
Scope of the Article: Clustering