An Improved Evaluation Methodology for Mining Association Rules

At present, association rules have been widely used in prediction, personalized recommendation, risk analysis and other fields. However, it has been pointed out that the traditional framework to evaluate association rules, based on Support and Confidence as measures of importance and accuracy, has several drawbacks. Some papers presented several new evaluation methods; the most typical methods are Lift, Improvement, Validity, Conviction, Chi-square analysis, etc. Here, this paper first analyzes the advantages and disadvantages of common measurement indicators of association rules and then puts forward four new measure indicators (i.e., Bi-support, Bi-lift, Bi-improvement, and Bi-confidence) based on the analysis. At last, this paper proposes a novel Bi-directional interestingness measure framework to improve the traditional one. In conclusion, the bi-directional interestingness measure framework (Bi-support and Bi-confidence framework) is superior to the traditional ones in the aspects of the objective criterion, comprehensive definition, and practical application.

A novel interestingness measure based on fusion model for association rules mining

Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules.

Implementasi Algoritma FP-Growth Untuk Strategi Pemasaran Ritel Hidroponik (Studi Kasus : PT. HAB)

Bisnis di bidang tanaman sayuran mengalami peningkatan yang cukup signifikan beberapa tahun belakangan ini. Salah satu cara untuk menghasilkan produk sayuran yang berkualitas tinggi secara kontinyu adalah budidaya dengan sistem hidroponik [1]. Bisnis hidroponik mempunyai peluang yang baik akan tetapi mempunyai kelemahan yaitu karena tanaman segar tanpa obat dan pengawet maka sayur dan buah hidroponik tidak dapat bertahan lama. Maka jika sayur dan buah ini tidak segera terjual akan mengakibatkan kerugian. Data mining merupakan proses mencari pola atau informasi menarik dalam data terpilih dengan menggunakan teknik atau metode tertentu. Apriori merupakan salah satu dari sepuluh algoritma yang paling berpengaruh dalam research community. Sejak algoritma Apriori pertama kali diperkenalkan, ada banyak upaya untuk merancang algoritma frequent itemset mining yang lebih efisien. Perbaikan yang paling menonjol pada Apriori menjadi sebuah metode yang disebut FP-Growth (frequent pattern growth) yang berhasil menghilangkan candidate generation [2]. Penelitian ini mengusulkan implementasi Algoritma FP-Growth dengan Software Open Source Weka untuk membantu menganalisa dan merancang katalog produk ritel hidroponik untuk mendorong buah atau sayur terjual secara bersama-sama. Dalam menentukan association rule, terdapat suatu interestingness measure (ukuran kepercayaan), yaitu support dan confidence. Penelitian ini, dengan menggunakan minimum suport 0,05 dan minimum confidence 0,9 menghasilkan 21 rule yang dapat digunakan sebagai strategi pemasaran PT. HAB.Kata Kunci: Algoritma FP-Growth, Strategi Pemasaran, Ritel Hidroponik.

Toward a Theory of Normalizing Function of Interestingness Measure of Binary Association Rules

Regarding the existence of more than sixty interestingness measures proposed in the literature since 1993 till today in the topics of association rules mining and facing the importance these last one, the research on normalization probabilistic quality measures of association rules has already led to many tangible results to consolidate the various existing measures in the literature. This article recommends a simple way to perform this normalization. In the interest of a unified presentation, the article offers also a new concept of normalization function as an effective tool for resolution of the problem of normalization measures that have already their own normalization functions.