José-María Serrano

Association rules applied to credit card fraud detection

Association rules are considered to be the best studied models for data mining. In this article, we propose their use in order to extract knowledge so that normal behavior patterns may be obtained in unlawful transactions from transactional credit card databases in order to detect and prevent fraud. The proposed methodology has been applied on data about credit card fraud in some of the most important retail companies in Chile.

TBAR: An efficient method for association rule mining in relational databases

Abstract In this paper, we propose a new algorithm for efficient association rule mining, which we apply in order to discover interesting patterns in relational databases. Our algorithm, which is called Tree-Based Association Rule mining (TBAR), redefines the notion of item and employs an effective tree data structure. It can also use techniques such as Direct Hashing and Pruning (DHP). Experiments with real-life datasets show that TBAR outperforms Apriori, a well-known and widely used algorithm.

ART: A Hybrid Classification Model

This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for ‘Association Rule Tree’, builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized “Separate and Conquer” algorithm suitable for Data Mining applications because it makes use of efficient and scalable association rule mining techniques.

AN ALTERNATIVE APPROACH TO DISCOVER GRADUAL DEPENDENCIES

In this paper we propose a new definition of gradual dependence as a special kind of association rule. We propose a way to adapt existing association rule mining algorithms for the new task of mining such dependencies, and we discuss about its complexity. Some experiments in a real database illustrate the usefulness of the approach.

Mining web documents to find additional query terms using fuzzy association rules

In this paper, we present an application of association rules to query refinement. Starting from an initial set of documents retrieved from the web, text transactions are constructed and association rules are extracted. A fuzzy extension of text transactions and association rules is employed, where the presence of the terms (items) in the documents (transactions) is determined with a value between 0 and 1. The obtained rules offer the user additional terms to be added to the query with the purpose of guiding the search and improving the retrieval.

A formal model for mining fuzzy rules using the RL representation theory

Data mining techniques managing imprecision are very useful to obtain meaningful and interesting information for the user. Among some other techniques, fuzzy association rules have been developed as a powerful tool for dealing with imprecision in databases and offering a good representation of found knowledge. In this paper we introduce a formal model for managing the imprecision in fuzzy transactional databases using the restriction level representation theory, a recent representation of imprecision that extends that of fuzzy sets. This theory introduces some new operators, keeping the usual crisp properties even when negation is involved. The model allows us to mine fuzzy association rules in a straightforward way, extending the accuracy measures from the crisp case. In addition, we introduce several ways of representing and summarizing the obtained results, in order to offer new and very interesting semantics. As an application, we present how to extract fuzzy association rules involving both the presence and the absence of items using the proposed model, and we also perform some experiments with real fuzzy transactional datasets.

A Complexity Guided Algorithm for Association Rule Extraction on Fuzzy DataCubes

The use of online analytical processing (OLAP) systems as data sources for data mining techniques has been widely studied and has resulted in what is known as online analytical mining (OLAM). As a result of both the use of OLAP technology in new fields of knowledge and the merging of data from different sources, it has become necessary for models to support imprecision. We, therefore, need OLAM methods which are able to deal with this imprecision. Association rules are one of the most used data mining techniques. There are several proposals that enable the extraction of association rules on DataCubes but few of these deal with imprecision in the process. The main problem observed in these proposals is the complexity of the rule set obtained. In this paper, we present a novel association rule extraction method that works over a fuzzy multidimensional model which is capable of representing and managing imprecise data. Our method deals with the problem of reducing the complexity of the result obtained by using fuzzy concepts and a hierarchical relation between them.

A definition for fuzzy approximate dependencies

In the analysis of data stored in databases, a very interesting issue is the detection of possible existing relations between attribute values and, at an upper level, relations between attributes themselves. In case uncertainty is present in data, or it is introduced in a pre-processing step, specific data mining and knowledge discovery techniques and methodologies must be provided. The theory of fuzzy subsets is a helpful tool to reach this goal. In this paper we introduce a new definition and an algorithm for computing fuzzy approximate dependencies, a type of relations that can be found between attributes in a fuzzy database, on the basis of a previous definition of fuzzy association rule. We will discuss about possible applications of this new tool.

Using association rules to mine for strong approximate dependencies

In this paper we deal with the problem of mining for approximate dependencies (AD) in relational databases. We introduce a definition of AD based on the concept of association rule, by means of suitable definitions of the concepts of item and transaction. This definition allow us to measure both the accuracy and support of an AD. We provide an interpretation of the new measures based on the complexity of the theory (set of rules) that describes the dependence, and we employ this interpretation to compare the new measures with existing ones. A methodology to adapt existing association rule mining algorithms to the task of discovering ADs is introduced. The adapted algorithms obtain the set of ADs that hold in a relation with accuracy and support greater than user-defined thresholds. The experiments we have performed show that our approach performs reasonably well over large databases with real-world data.

Association Rule Extraction for Text Mining

We present the definition of fuzzy association rules and fuzzy transactions in a text framework. The traditional mining techniques are applied to documents to extract rules. The fuzzy framework allows us to deal with a fuzzy extended Boolean model. Text mining with fuzzy association rules is applied to one of the classical problems in Information Retrieval: query refinement. The extracted rules help users to query the system by showing them a list of candidate terms to refine the query. Different procedures to apply these rules in an automatic and semi-automatic way are also presented.