Interpretations of Association Rules by Granular Computing

We present interpretations for association rules. We first introduce Pawlak's method, and the corresponding algorithm of finding decision rules (a kind of association rules). We then use extended random sets to present a new algorithm of finding interesting rules. We prove that the new algorithm is faster than Pawlak's algorithm. The extended random sets are easily to include more than one criterion for determining interesting rules. We also provide two measures for dealing with uncertainties in association rules

Mining for Useful Association Rules Using the ATMS

Association rule mining has made many achievements in the area of knowledge discovery in databases. Recent years, the quality of the extracted association rules has drawn more and more attention from researchers in data mining community. One big concern is with the size of the extracted rule set. Very often tens of thousands of association rules are extracted among which many are redundant thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a novel ATMS-based method for extracting non-redundant association rules

Analisis Association Rules Algoritma Apriori Penjualan Kaos Travelling

Perusahaan atau USAha industri adalah unit USAha yang melakukan kegiatan ekonomi, bertujuan menghasilkan barang atau jasa, terletak pada suatu bangunan atau lokasi tertentu, dan mempunyai catatan administrasi tersendiri mengenai produksi dan struktur biaya serta ada seorang atau lebih yang bertanggung jawab atas USAha tersebut. Salah satu golongan industri yang mempunyai peran penting dalam perekonomian Provinsi DIY adalah industri tekstil dan pakaian jadi. Pasang surut industri ini di tingkat nasional juga berdampak di tingkat daerah. Selain itu, industri ini juga menghadapi persaingan yang ketat mengingat sudah banyak yang menjalankan bisnis seperti ini dimasyarakat ditambah dengan banyaknya produk tekstil dan pakaian jadi impor yang masuk di pasaran Indonesia. Keadaan ini juga di alami oleh salah satu Perusahaan konveksi yang ada di DI. Yogyakarta yakni adalah Distro Indonesia. Untuk membantu meningkatkan penjualan di Distro Indonesia tersebut, diperlukan solusi untuk mendapatkan gambaran mengenai hubungan antar produk yang sering dibeli oleh customer. Metode analisis yang dapat digunakan untuk mengetahui pola hubungan suatu produk salah satunya adalah Association Rules Algoritma Apriori. Hasil yang diperoleh terdapat sebelas aturan asosiasi yang terbentuk, dengan batasan nilai minimum support yaitu sebesar 0,01 dan batasan nilai minimum confidence yaitu sebesar 0,4. Sedangkan dengan batasan nilai minimum confidence 0,4 serta minimum support 0.02, diperoleh aturan asosiasi yang terkuat yakni jika seorang pembeli membeli barang dengan kode Hiking Rules maka pembeli tersebut juga membeli barang dengan kode My Trip My Adventure

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

Temporal fuzzy association rule mining with 2-tuple linguistic representation

This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules

Towards a semantic and statistical selection of association rules

The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. Association rules selection is a classical topic to address this issue, yet, new innovated approaches are required in order to provide help to decision makers. Hence, many interesting- ness measures have been defined to statistically evaluate and filter the association rules. However, these measures present two major problems. On the one hand, they do not allow eliminating irrelevant rules, on the other hand, their abun- dance leads to the heterogeneity of the evaluation results which leads to confusion in decision making. In this paper, we propose a two-winged approach to select statistically in- teresting and semantically incomparable rules. Our statis- tical selection helps discovering interesting association rules without favoring or excluding any measure. The semantic comparability helps to decide if the considered association rules are semantically related i.e comparable. The outcomes of our experiments on real datasets show promising results in terms of reduction in the number of rules