Involvement of mitochondrial and nuclear gene mutations in the development of type 2 diabetes (T2D) has been established well in various populations around the world. Previously, we have found the mitochondrial A>G transition at nucleotide position 3243 and 8296 in the T2D patients of Coimbatore population. This study is aimed to screen for the presence of various mitochondrial and nuclear DNA mutations in the T2D patients of Coimbatore to identify most prevalent mutation. This helps in identifying the susceptible individuals based on their clinical phenotype in future. Blood samples were collected from 150 unrelated late-onset T2D patients and 100 age-matched unrelated control samples according to World Health Organization criteria. Genotyping for the selected genes was done by polymerase chain reaction-single strand confirmation polymorphism, direct sequencing, and polymerase chain reaction-restriction fragment length polymorphism. The mitochondrial T>C transition at 8356 and nuclear-encoded GLUT1 gene mutation were found in the selected T2D patients. The T8356C mutation was found in two patients (1.3%), and the clinical characteristics were found to be similar in both the patients whereas GLUT1 gene mutation was found in seven patients. Four out of seven patients showed homozygous (-) genotype and three patients showed heterozygous (±) genotype for the mutant allele XbaI. Among these three patients, one patient was found to have elevated level of urea and creatinine with the history of kidney dysfunction and chronic T2D. Our results suggest that the T8356C and GLUT1 gene mutations may have an important role in developing late-onset T2D in Coimbatore population. Particularly, individuals with GLUT1 gene may develop kidney dysfunction at their later age.

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In recent days, Data Mining (DM) is an emerging area of computational intelligence that provides new techniques, algorithms and tools for processing large volumes of data. Clustering is the most popular data mining technique today. Clustering used to separate a dataset into groups that finds intra-group similarity and inter-group similarity. Outlier detection (Anomaly) is to find small groups of data objects that are different when compared with rest of data. The outlier detection is an essential part of mining in data stream. Data Stream (DS) used to mine continuous arrival of high speed data Items. It plays an important role in the fields of telecommunication services, E-Commerce, Tracking customer behaviors and Medical analysis. Detecting outliers over data stream is an active research area. This survey presents the overview of fundamental outlier detection approaches and various types of outlier detection methods in data stream.

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