International Journal of Computer Applications (0975 – 8887) Volume 62– No.8, January 2013 42 A Review: Data Mining over Multi-Relations Deepak Meena Research Scholar Patel Institute of Technology Bhopal Hitesh Gupta Head, CSE, Dept Patel Institute of Technology Bhopal ABSTRACT In this paper, Multi-relational data mining enables pattern mining from multiple tables. Multi-relational data mining algorithms can be used as practical proposal to overcome the deficiency of conventional algorithms. Multi-relational data mining algorithms directly extract frequent patterns from different registers in efficient manner without need of transfer the data in a single table will, on the other hand, used the available memory space is not enough to ensure the production of large amounts of data. For this reason, and the use of space, algorithms are an integral care for the prospection of large repositories. The paper provides the overview of multi relation data mining techniques and classification algorithms. It also defines the frequent pattern mining. The presented paper discussed the various architecture and issues related to multi table data mining. A lot of literature has been proposed in this area. Some of them has discussed in this paper. Keywords Data mining, multi relation classification, FP tree. 1. INTRODUCTION The most of the available data mining technique are appropriate for a single relation of database. Now there may be some difficulty in order to use the multi relation of database. To perform join operation in database is a time consuming task. Ones the join operation applied then there is a possibility to lost some information or data may be repeated in database. So how can this complexity reduce or remove in order to get the meaningful patterns for multiple tables. Data mining is a rapid growth field in order to get useful information from database [1]. Data mining includes the foundation of probability; classical machine learning algorithms which belong to data mining. Association rule is also an important part of data mining. It has used in order to present the knowledge. If there is relation between itemsets and huge number of transactions then there is possibility of association rule mining. The researchers are working on it. It seems to be that there are two problems in traditional association rule mining. First, traditional methods suppose items in transaction database have same significance, thus the mining process is flooded in the combinatorial explosion of insignificant relationships. The mining result may comprise duplicate information and consume lots of space. Second, those methods ignore the difference between two items, which may lead to incorrect result biased with users' expectation in real applications. Frequent pattern mining [7], [13] plays a crucial role in many data mining tasks like data mining association rules, discovering patterns having useful correlations, constraint based patterns etc. 2. DATA MINING A knowledge Discovery process is known as the data mining technique. Data mining is used to deal with huge data which are in the database, it is use in order to find the desired information and knowledge from the database. Data Mining is more oriented towards applications than the basic nature of the underlying phenomena. In other words, Data Mining is relatively less concerned with identifying the specific relations between the involved variables. There are three basic steps in order to perform the data mining. (1) The initial exploration, (2) model building or pattern identification (3) deployment There are many data mining techniques has proposed by the researchers such as, decision trees, association rules, and neural networks etc. The other most useful data mining technique appear in order to find patterns is multi-relational data mining (MRDM) approach. It includes multiple tables or relations from the given relational database [1,2]. Association, classification, clustering, prediction and sequential patterns are also some other techniques used in data mining. Data mining is not only using in business ambiance but also in other world such as weather forecast, healthcare, insurance, medicine, transportation etc. data mining can also use in banking, finance, retail and marketing. But some time there may be problem like privacy issues and security issues. Figure 1 : Simple Working of Data mining Technique The above figure shows the how the data mining method works on the raw data which is stored in database.