Mining data quality rules based on T-dependence

Since their introduction in 1976, edit rules have been a standard tool in statistical analysis. Basically, edit rules are a compact representation of non-permitted combinations of values in a dataset. In this paper, we propose a technique to automatically find edit rules by use of the concept of T-dependence. We first generalize the traditional notion of lift, to that of T-lift, where stochastic independence is generalized to T-dependence. A combination of values is declared as an edit rule under a t-norm T if there is a strong negative correlation under T-dependence. We show several interesting properties of this approach. In particular, we show that under the minimum t-norm, edit rules can be computed efficiently by use of frequent pattern trees. Experimental results show that there is a weak to medium correlation in the rank order of edit rules obtained under T_M and T_P, indicating that the semantics of these kinds of dependencies are different

Inference of mixed information in Formal Concept Analysis

Negative information can be considered twofold: by means of a negation operator or by capturing the absence of information. In this second approach, a new framework have to be developed: from the syntax to the semantics, including the management of such generalized knowledge representation. In this work we traverse all these issues in the framework of formal concept analysis, introducing a new set of inference rules to manage mixed (positive and negative) attributes.TIN2014-59471-P of the Science and Innovation Ministry of Spain, co-funded by the European Regional Development Fund (ERDF). UNIVERSIDAD DE MÁLAGA. Campus de Excelencia Internacional Andalucía Tech

Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining

We present theoretical analysis and a suite of tests and procedures for addressing a broad class of redundant and misleading association rules we call \emph{specious rules}. Specious dependencies, also known as \emph{spurious}, \emph{apparent}, or \emph{illusory associations}, refer to a well-known phenomenon where marginal dependencies are merely products of interactions with other variables and disappear when conditioned on those variables. The most extreme example is Yule-Simpson's paradox where two variables present positive dependence in the marginal contingency table but negative in all partial tables defined by different levels of a confounding factor. It is accepted wisdom that in data of any nontrivial dimensionality it is infeasible to control for all of the exponentially many possible confounds of this nature. In this paper, we consider the problem of specious dependencies in the context of statistical association rule mining. We define specious rules and show they offer a unifying framework which covers many types of previously proposed redundant or misleading association rules. After theoretical analysis, we introduce practical algorithms for detecting and pruning out specious association rules efficiently under many key goodness measures, including mutual information and exact hypergeometric probabilities. We demonstrate that the procedure greatly reduces the number of associations discovered, providing an elegant and effective solution to the problem of association mining discovering large numbers of misleading and redundant rules.Comment: Note: This is a corrected version of the paper published in SDM'17. In the equation on page 4, the range of the sum has been correcte

A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen-dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on asso-ciation rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the fre-quency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages those are not yet visited by users are not included in the recommendation set. To over-come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso-ciation rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table