IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 4, 2013 | ISSN (online): 2321-0613 All rights reserved by www.ijsrd.com 812 Abstract—The time required for generating frequent patterns plays an important role. Some algorithms are designed, considering only the time factor. Our study includes depth analysis of algorithms and discusses some problems of generating frequent pattern from the various algorithms. We have explored the unifying feature among the internal working of various mining algorithms. The work yields a detailed analysis of the algorithms to elucidate the performance with standard dataset like Mushroom etc. The comparative study of algorithms includes aspects like different support values, size of transactions. I. INTRODUCTION The term data mining or knowledge discovery in database has been adopted for a field of research dealing with the automatic discovery of implicit information or knowledge w i t h i n t h e databases. The implicit information within databases, mainly the interesting association relationships among sets of objects that lead to association rules may disclose useful patterns for decision support, financial forecast, marketing policies, even medical diagnosis and many other applications. The development of tools capable in the automatic extraction of knowledge from data. To analyze the huge amount of data thereby exploiting the consumer behavior and make the correct decision leading to competitive edge over rivals[1]. The problem of mining frequent item sets arose first as a sub problem of mining association rules. A priori algorithm is quite successful for market based analysis in which transactions are large but frequent items generated is small in number 2 . Frequent itemsets play an essential role in many data mining tasks that try to find interesting patterns from databases such as association rules, correlations, sequences, classifiers, clusters and many more of which the mining of association rules is one of the most popular problems. Also Sequential association rule mining is one of the possible methods to analysis of data used by frequent itemsets3. The original motivation for searching association rules came from the need to analyze so called supermarket transaction data, that is, to examine customer behavior in terms of the purchased products. Association rules describe how often items are purchased together. For example, associations rule “beer, chips (60%)” states that four out of five customers that bought beer also bought chips. Such rules can be useful for decisions concerning product pricing, promotions, store layout and many others[4]. A. Frequent itemset mining problem Studies of Frequent Pattern Mining is acknowledged in the data mining field because of its broad applications in mining association rules, correlations, and graph pattern constraint based on frequent patterns, sequential patterns, and many other data mining tasks. Efficient algorithms for mining frequent patterns are crucial for mining association rules as well as for many other data mining tasks. The major challenge found in frequent pattern mining is a large number of result patterns. As the minimum threshold becomes lower, an exponentially large number of patterns are generated. Therefore, pruning unimportant patterns can be done effectively in mining process and that becomes one of the main topics in frequent pattern mining. Consequently, the main aim is to optimize the process of finding patterns which should be efficient, scalable and can detect the important patterns which can be used in various ways 5 . II. METHODOLOGY The methodology used to mine frequent itemsets denoted (Figure1). A. FP-Growth Algorithm FP-tree algorithm is based upon the recursively divide and conquers strategy; first the set of frequent 1-itemset and their counts is discovered. With start from each frequent pattern, construct the conditional pattern base, then its conditional FP-tree is constructed (which is a prefix tree.). Until the resulting FP-tree is empty, or contains only one single path. (Single path will generate all the combinations of its sub-paths, each of w h i c h i s a frequent pattern). The items i n e a c h transaction are processed in L order. (i.e. items in the set were sorted based on their frequencies in the descending order to form a list)[6]. The detail step is as shown in figure1: Create root of the tree as a “null”. After scanning the database D for finding the 1-itemset then process the each transaction in decreasing order of their frequency. A new branch is created for each transaction with the corresponding support. If same node is encountered in another transaction, just increment the support count by one of the common node. Each item points to the occurrence in the tree using the chain of node-link by maintaining the header table. After above process mining of the FP-tree will be done by Creating Conditional (sub) pattern bases: Start from node constructs its conditional pattern base. Then, Construct its conditional FP-tree and FP-Growth Method: Construction of FP-tree A Study of Various Projected Data Based Pattern Mining Algorithms Ashish Prajapati 1 Prof. Jigar Patel 2 1, 2 Alpha College of Eng. and technology Khatraj, Ahmadabad, India