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Outlier Detection in Cancer Infected Cells by Random Forest Approach and Distance-Based Outliers


S. Mohamad Haja Sherif , B.S.Abdur Rahman University; C.Imthyaz Sheriff, B.S.Abdur Rahman University


Biomedical Text Mining Phases and Tasks, Cluster Analysis Based on Frequent Pattern


In the recent past, large scale databases and files have grown beyond the capabilities and capacities of commercial database management systems. Parallel processing is very much essential to process a massive volume of data in a timely manner. Field of healthcare and medical industry is always in need for newer ways of analysing and making senses of the data available with them. Cancer related data analytics is always pushing it frontiers in deploying effective and efficient ways of analysing cancer related data. This paper deals with usage of random forest approach and distance based outlier’s detection for cancer detection in large datasets

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Paper ID: IJSRDV3I2674
Published in: Volume : 3, Issue : 2
Publication Date: 01/05/2015
Page(s): 1196-1199

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