FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases

In recent years, discovery of association rules among itemsets in a large database has been described as an important database-mining problem. The problem of discovering association rules has received considerable research attention and several algorithms for mining frequent itemsets have been developed. Many algorithms have been proposed to discover rules at single concept level. However, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from data. The discovery of multiple level association rules is very much useful in many applications. In most of the studies for multiple level association rule mining, the database is scanned repeatedly which affects the efficiency of mining process. In this research paper, a new method for discovering multilevel association rules is proposed. It is based on FP-tree structure and uses cooccurrence frequent item tree to find frequent items in multilevel concept hierarchy.Comment: Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

Market basket analysis of library circulation data

“Market Basket Analysis” algorithms have recently seen widespread use in analyzing consumer purchasing patterns-specifically, in detecting products that are frequently purchased together. We apply the Apriori market basket analysis tool to the task of detecting subject classification categories that co-occur in transaction records of book borrowed form a university library. This information can be useful in directing users to additional portions of the collection that may contain documents relevant to their information need, and in determining a library’s physical layout. These results can also provide insight into the degree of “scatter” that the classification scheme induces in a particular collection of documents

Model Rule: Multilevel And Multidimension Association Rule untuk Analisa Market Basket Pada PT. Maha Agung

PT. Maha Agung adalah sebuah perusahaan distribusi yang memiliki gudang distribusi tersebar di lima daerah yang berbeda. Karena besarnya area pasar, perusahaan bukan hanya membutuhkan informasi keterkaitan antara produknya saja, akan tetapi juga faktor waktu, wilayah pemasaran, profil pelanggan, dan lain sebagainya. Oleh sebab itu model rule stardart yang dihasilkan oleh metode analisa market basket, yaitu: single level, multilevel serta multidimesion association rule tidak dapat digunakan. Pada penelitian ini diusulkan untuk mengkombinasi dua macam model rule, yaitu: Multilevel Association Rule serta Multidimesion Association Rule menjadi bentuk lain. Aplikasi yang dibuat pada penelitian ini menghasilkan sebuah model association rule baru yang kita namakan "Multilevel And Multidimension Association Rule". Pemanfaatan model association rule baru ini untuk menjawab kebutuhan PT. Maha Agung ini terbukti tepat. Hal ini dapat dari hasil kuisioner calon pemakai yang cukup baik, yaitu sebesar 89.6%. Kata Kunci: Data Mining, Analisa Market Basket, Multilevel And Multidimension Association Rul

Literature Review on Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases

This paper presenting a survey on finding itemsets with high utility. For finding itemsets there are many algorithms but those algorithms having a problem of producing a large number of candidate itemsets for high utility itemsets which reduces mining performance in terms of execution. Here we mainly focus on two algorithms utility pattern growth (UP-Growth) and UP-Growth+. Those algorithms are used for mining high utility itemsets, where effective methods are used for pruning candidate itemsets. Mining high utility itemsets Keep in a special data structure called UP-Tree. This, compact tree structure, UP-Tree, is used for make possible the mining performance and avoid scanning original database repeatedly. In this for generation of candidate itemsets only two scans of database. Another proposed algorithms UP Growth+ reduces the number of candidates effectively. It also has better performance than other algorithms in terms of runtime, especially when databases contain huge amount of long transactions. Utility-based data mining is a new research area which is interested in all types of utility factors in data mining processes. In which utility factors are targeted at integrate utility considerations in both predictive and descriptive data mining tasks. High utility itemset mining is a research area of utility based descriptive data mining. Utility based data mining is used for finding itemsets that contribute most to the total utility in that database

A Mining Algorithm under Fuzzy Taxonomic Structures

Most conventional data-mining algorithms identify the relationships among transactions using binary values and find rules at a single concept level. Transactions with quantitative values and items with taxonomic relations are, however, commonly seen in real-world applications. Besides, the taxonomic structures may also be represented in a fuzzy way. This paper thus proposes a fuzzy multiple-level mining algorithm for extracting fuzzy association rules under given fuzzy taxonomic structures. The proposed algorithm adopts a top-down progressively deepening approach to finding large itemsets. It integrates fuzzy-set concepts, data-mining technologies and multiple-level fuzzy taxonomy to find fuzzy association rules from given transaction data sets. Each item uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of the original items. The algorithm therefore focuses on the most important linguistic terms for reduced time complexit

Generating Non-redundant Multilevel Association Rules Using Min-max Exact Rules

Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets