Investigation of Pattern Mining Using Formal Language and Dynamic Function

Abstract

In present scenario almost every system and activity is computerized and newlinehence all information and data are being stored in Computers. The capacity of data newlinestorage is in increasing trend. The amount of data is also increasing in line with data newlinestorage capacity. The huge collections of data are emerging. Retrieval of newlineuntouched, hidden and important information from this huge data is a tedious work. newlineData Mining Provides solution which extracts untouched, hidden and important newlineinformation from vast databases to investigate noteworthy knowledge in the data newlinewarehouse. An important issue in data mining is to discover patterns in various newlinefields like medical science, World Wide Web, telecommunication, etc. newlineThe database of pathological data in which experiments of genome biology newlinecontains attributes are very high as compared to experiments. In this situation newlinewhere transaction are less and attributes are very large, applying data mining newlinealgorithm requires less search space. Most of the preceding work on vertical newlinemining focuses on intersection of bit-vector of transaction id. List-based layout newlinetakes much less space than the bit-vector approach. We use list-based layout and newlineinstead of brute force method for intersection we use dynamic function of longest newlinecommon subsequence problem, which takes polynomial time. newlineWe proposed a new apriori like algorithm (termed DFPMT-A Dynamic newlineapproach for frequent patterm mining using transposition of database) for frequent newlinepattern mining. The main feature of proposed algorithm is that the database newlinerepresented in transpose form and in each step stored database is filtered by newlinegenerating longest common subsequence of transaction id for each pattern using newlinedynamic approach. The proposed algorithm gives better results as compared to newlineexisting algorithms. Further it works efficiently for sparse database also. newlineMost of the existing apriori-like approaches are based on generate and test candidate theme.