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Despite the significance of data mining techniques to Heart Disease Data (HDD), there be a short of comprehensive literature review and a classification for it. This is the particular academic literature review of the application of data mining techniques to HDD (Heart Disease Data). It provides an academic database of literature amid the periods of 20062016 and proposes a classification scheme to classify the Mining techniques. The analysis and classification process was autonomously verified. Findings of this paper indicate that the research area of Heart Disease received most research attention. Classification and Association models are the two most commonly used models for data mining in Heart Disease Data. Our study provides a path to provide future research ideas and make easy creation about the application of data mining techniques in HDD.

clustering is a process in which we group the data by finding similarities between data based on their characteristics. These groups are called cluster. In clustering, there is a division of data into groups of similar objects. These groups are the clusters, consists of objects that are similar between themselves and dissimilar compared to objects of other groups. Clustering is unsupervised learning technique, based on the concept of maximize intra-clustering and minimize inter- clustering. Nowadays, clustering of biological dataset is the widely researched topic among computer science. Bio- informatics has become area that receive most of the attention of data mining techniques. Generally, bio- informatics targets to solve complicated problems like gene categorization and its functionality, gene expression analysis of data obtained from micro- array experiments etc. These clustering techniques are addressed with R. Clustering techniques are used to analyze the structure of biological data. There are many different methods but we study k- means, Hierarchical and Density- based clustering algorithm for Biological Data using R programming tool.

Our ability to assemble genome-wide expression data has far exceeded the ability of our diminutive human brains to analyses the raw data. We can refine the data down to a more understandable level by grouping the genes into a smaller number of categorization and then analyzing those. Clustering is one of the most popular techniques in gene expression. Many clustering algorithms have been initiated to analyze gene expression data, but less supervision is available to help choose among them. The evaluation of feasible and applicable clustering algorithms is becoming an important issue in today’s bioinformatics research. It is a method of grouping data into different clusters, in which each cluster must have data of great similarity and high dissimilarity with other cluster data. The basic clustering algorithms are K-mean and K-medoids. This paper presents a literature survey on the clustering application in gene expression. An attempt is made to provide a guide for the researchers who are working in the area of gene expression and data clustering.