Data Mining is defined as extracting the data from large datasets. The main goal of data mining is extracting the information from data sets and transforms it to an understandable structure for future works. The actual data mining task is the automatic analysis of large data sets. It has been classified into cluster analysis (group of records), unusual records (anomaly detection), and dependencies (association rules mining). Data mining uses information from past data to analyze the outcome of a particular problem or situation that may arise. Data mining works to analyze data stored in data warehouses that are used to store that data that is being analyzed. Data mining (the analysis step of the “Knowledge Discovery in Databases” process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining uses information from past data to analyze the outcome of a particular problem or situation that may arise. Data mining works to analyze data stored in data warehouses that are used to store that data that is being analyzed. That particular data may come from all parts of business, from the production to the management. Managers also use data mining to decide upon marketing strategies for their product. They can use data to compare and contrast among competitors. Data Mining is the nontrivial process of identifying valid, novel, potentially useful and ultimately understandable pattern in data with the wide use of databases and the explosive growth in their sizes. Data mining refers to extracting or “mining” knowledge from large amounts of data. Data mining is the search for the relationships and global patterns that exist in large databases but are hidden among large amounts of data. The essential process of Knowledge Discovery is the conversion of data into knowledge in order to aid in decision making, referred to as data mining. Knowledge Discovery process consists of an iterative sequence of data cleaning, data integration, data selection, data mining pattern recognition and knowledge presentation . Data Mining has great potential for exploring the meaningful and hidden patterns in the data sets at the medical domain. Discovery of hidden patterns and relationships often goes unexploited. Advanced data mining techniques is a remedy to this situation. Data mining functions include clustering, classification, prediction, and associations. One of the most important data mining applications is that of mining association rules. Association rules, first introduced in 1993, are used to identify relationships among a set of items in databases. These relationships are not based on inherit properties of the data themselves, but rather based on co-occurrence of the data items [2]. Emphasis in this research work is analysis of medical data. Medical profiles such as patient name, age, sex, disease name, address, time, date, etc., can be used to mining the frequent disease of patients in different geographical area at given time period.