HYPER METHOD BY USE ADVANCE MINING ASSOCIATION RULES ALGORITHM

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1 HYPER METHOD BY USE ADVANCE MINING ASSOCIATION RULES ALGORITHM Media Noaman Solagh 1 and Dr.Enas Mohammed Hussien 2 1,2 Computer Science Dept. Education Col., Al-Mustansiriyah Uni. Baghdad, Iraq Abstract-The paper proposed anew algorithms based on FP-tree structure for mining association rules at first MFFP-growth algorithm use with HFMFFP-growth algorithm, than lead to get a tree with less node than the original FP-tree. HFMFFP-Growth divided dataset to portions and for each part take most frequent item and than build the tree, the final result is more efficiency than FPgrowth. I. INTRODUCTION Association rule mining has been one of the most popular data-mining subjects, which can be simply defined as finding interesting rules from large collections of data. Association rule mining has a wide range of applicability such Market basket analysis, Medical diagnosis/ research, Website navigation analysis, Homeland security and so on. Association rules are used to identify relationships among a set of items in database. These relationships are not based on inherent properties of the data themselves (as with functional dependencies), but rather based on cooccurrence of the data items [6]. first presented a model to address the problem of mining association rules. Given a transaction database, the definition of mining association rules is to discover the important rules that apply to items. Let DB = {T1, T2,..., Tn} represent the transaction database, which comprises set of transactions of variable length. Let I = {i1, i2, im} be a set of items (itemset). Each transaction T constitutes a subset of I. The associated unique identifier of each transaction is called its TID [11]. Association rule and frequent itemset mining became a widely researched area, and hence faster and faster algorithms have been presented and Frequent pattern mining techniques can also be extended to solve many other problems, such as iceberg-cube computation and classification Thus the effective and efficient frequent pattern mining is an important and interesting research problem [8]. II. RESEARCH WORK In 2013, Performance Measure of Similis and FP-Growth Algorithm, in this study show the mining association rules is one of the main application areas of Data Mining, here use market basket dataset, at present there are various Association Rules Algorithms are in market. This paper define the survey done on various algorithms of Association Rules of Data Mining and also compare two main algorithms-similis Algorithm and FP-Growth Algorithm depending upon the different criteria that included in paper.[1]. In 2013, FP-Growth Approach to Mining Association Rules, in this paper of data mining researchers implements lots of algorithms for improving the performance of mining. This work is also related to that strategy. This work, introduce an idea in this field. Here use Sampling Technique to convert text document in to the appropriate format. This format contains data in the form of word and topic of word. This format take as a input in FP-Growth algorithm for given support value and get association rules of that transaction data, and after getting association rules apply clustering process and then get clusters for that association rules.[9]. In 2011, PAMARF algorithm, here a parallel algorithm for mining association rules based on FPtree, namely, PAMARF algorithm. It proposed by distributed data according horizontal projection DOI: /IJMTER MJONO 165

2 method. PAMARF algorithm made nodes compute local frequent itemsets with FP-tree, then the centre node exchanged data with other nodes and combined, finally, global frequent itemsets were gained, most of them divide global transaction database into equal n fraction according to horizontal method [9]. III. FRAME WORK One of the currently fastest and most popular algorithms for frequent item set mining is the FP-growth algorithm. It is based on a prefix tree representation of the given database of transactions (called an FP-tree), which can save considerable amounts of memory for storing the transactions [21]. Similar to several other algorithms for frequent item set mining, like, for example, Apriori, FP-growth preprocesses the transaction database as follows: in an initial scan the frequencies of the items (support of single element item sets) are determined. All infrequent items that is, all items that appear in fewer transactions than a user-specified minimum number are discarded from the transactions, since, obviously, they can never be part of a frequent item set [6] [27]. In addition, the items in each transaction are sorted, so that they are in descending order their frequency in the database. Although the algorithm does not depend on this specific order, experiments showed that it leads to much shorter execution times than a random order. An ascending order leads to a particularly slow operation in my experiments, performing even worse than a random order [21] [28]. The FP-tree is a prefix-tree structure that stores information about each frequent 1-itemset, in which the items are arranged in order of decreasing support value. Then, the mining process is transformed to mine the FP-tree [16][38]. 3.1 MFFP-growth algorithm The tree structure of the MFFP-growth (MFFP-tree) can be considered as extracting the subtree MF (MF denotes the most frequent 1-itemset) from the FP-tree. Other subtrees of the root are merged with the MF subtree. Additionally, the MF node becomes the root of the MFFP-tree. Therefore, the nodes in the MFFP-tree are fewer than those of the fp-tree. counter of the MFFP-tree records the same information as the FP-tree, pattern-growth algorithm can be applied to generate all frequent itemsets. An MFP-tree can be constructed as follows 1. scan the D once, Store all the frequent 1-itemsets F and their individual support values. 2. Sort F in order of decreasing support value to generate a list of frequent 1-itemsets H. 3. Select the most frequent 1-itemset (MF) to generate the root node of the MFFP-tree, labeled MF. 4. For each transaction T DB, perform the following; Select the frequent 1-itemsets in T and sort them in order of H. Let the frequent 1-itemsets list in T be {m1, m2,..., mn}. If m1 == MF, call function Insert_node(m2, root); else call function Insert_ node(m1, All rights Reserved 166

3 3.2 Horizontal Fragmentation of most frequent FP-growth algorithm This method was proposed based horizontal fragmentation of dataset to equal size HFMFFP-growth is a hyper method between horizontal fragmentation for dataset with MFFP-growth algorithm and this step can be summarized as follow Example Consider the transaction database in Table with a minimum support threshold of 30%. First divided dataset to equal portions and scans the all sub database in same time to discover all frequent1-itemsets and sorts these 1-itemsets in order of descending frequency of occurrence this step is similar to traditional FP-growth, than use MFFP-growth algorithm to find association rules from each part of sub dataset. TID Transactions A1 B1 C1 D1 F1 I1 J1 K1 A1 B2 C1 D1 F1 G H I1 J2 K2 A1 B2 C2 D1 F1 G I1 J1 K2 L A1 B2 C1 D1 F1 G H I2 J2 K2 L Table: example of database Frequent itemset (orderd) A1,D1,F1,C1,I1,J1,B1,K A1,D1,F1,B2,C1,G,I1,K2,H,J A1,D1,F1,B2,G,I1,K2,J1,L,C All rights Reserved 167

4 Figure: tree structure IV. EXPERIMENTAL RESULTS The performance of HFMFFP-growth with MFFP-growth is more efficient for miming frequent itemsets,based on horizontal fragmentation technique with most frequent itemset. memory space for HFMFF-tree in byte less than memory space for FP-growth and MFFP-growth. Test this algorithm on 30% and 50% minsup,using for coded visual.net, and the dataset is real dataset from UCI machine learning, use bank marketing dataset after preprocessing dataset. Full dataset 4500 records in figure.show computational performance of mining the bank marketing dataset between FP-growth, MFFP-growth and HFMFFP-growth dataset result 0 30% 50% FP-growth MFFP-growth HFMFFP-growth V. CONCLUSION This research suggested a new method to more efficiency performance than original FPgrowth is summarized by mix between MFFP-growth algorithm and HFMFFP-growth algorithm, this mix lead to reduce memory space and execution All rights Reserved 168

5 REFERENCES [1] M. H. Margahny and A. Shakour," FAST ALGORITHM FOR MINING ASSOCIATION RULES", Journal of Engineering Sciences, Assiut University, Vol. 34, No. 1, pp , January [2] Vasilis Aggelis Dimitris Christodoulakis," Association Rules and Predictive Models for e-banking Services", Department of Computer Engineering and Informatics University of Patras, Rio, Patras, Greece. [3] Kenneth Lai, Narciso Cerpa," Support vs Confidence in Association Rule Algorithms". [4] Singh, J. Agarwa, A. Rana," Performance Measure of Similis and FP-Growth Algorithm", International Journal of Computer Applications, January [5] Rakesh Kumar Soni1, Prof. Neetesh Gupta2, Prof. Amit Sinhal3,"An FP-Growth Approach to MiningAssociation Rules ", IJCSMC, Vol. 2, Issue. 2, February 2013, pg.1 5. [6] María N. Moreno, Saddys Segrera and Vivian F. López," Association Rules Problems solutions and new applications", Universidad de Salamanca, Plaza Merced S/N, 37008, All rights Reserved 169

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