Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing

This paper presents a multiobjective genetic algorithm which obtains fuzzy rules for subgroup discovery in disjunctive normal form. This kind of fuzzy rules lets us represent knowledge about patterns of interest in an explanatory and understandable form which can be used by the expert. The evolutionary algorithm follows a multiobjective approach in order to optimize in a suitable way the different quality measures used in this kind of problems. Experimental evaluation of the algorithm, applying it to a market problem studied in the University of Mondragón (Spain), shows the validity of the proposal. The application of the proposal to this problem allows us to obtain novel and valuable knowledge for the experts.

Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms

Evolutionary algorithms for subgroup discovery in e-learning: A practical application using Moodle data

Supervised descriptive rule discovery techniques like subgroup discovery are quite popular in applications like fraud detection or clinical studies. Compared with other descriptive techniques, like classical support/confidence association rules, subgroup discovery has the advantage that it comes up with only the top-k patterns, and that it makes use of a quality function that avoids patterns uncorrelated with the target. If these techniques are to be applied in privacy-sensitive scenarios involving distributed data, precise guarantees are needed regarding the amount of information leaked during the execution of the data mining. Unfortunately, the adaptation of secure multi-party protocols for classical support/confidence association rule mining to the task of subgroup discovery is impossible for fundamental reasons. The source is the different quality function and the restriction to a fixed number of patterns – i.e. exactly the desired features of subgroup discovery. In this paper, we present a new protocol which allows distributed subgroup discovery while avoiding the disclosure of the individual databases. We analyze the properties of the protocol, describe a prototypical implementation and present experiments that demonstrate the feasibility of the approach.KeywordsAssociation RuleQuality FunctionPrivacy PreserveSubgroup DiscoveryPrivacy Preserve Data MiningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Many Evolutionary Algorithms have been proposed to solve the Subgroup Discovery task. Some of these, however, have been shown to work poorly in high dimensional problems. The best performing evolutionary algorithm for subgroup discovery in high dimensional datasets has a particular way to initialize its starting population, limiting the size of initial solutions to the lowest possible value. As with most population-based techniques, the outcome of evolutionary algorithms is usually dependent on the initial set of solutions, which are typically randomly generated. The impact of choosing one initialization technique over another in the final presented solution has been the topic of many published works in the broad area of evolutionary computation. However, to the best of our knowledge, it has not been the topic of study in the specific case of the Subgroup Discovery task, especially when considering high dimensional datasets. Therefore, this paper aims at studying whether or not it is possible to improve the performance of evolutionary algorithms in high dimensional subgroup discovery tasks by biasing the initial population to individuals with lower sizes.

Subgroup discovery is a learning task that aims at finding interesting rules from classified examples. The search is guided by a utility function, trading off the coverage of rules against their statistical unusualness. One shortcoming of existing approaches is that they do not incorporate prior knowledge. To this end a novel generic sampling strategy is proposed. It allows to turn pattern mining into an iterative process. In each iteration the focus of subgroup discovery lies on those patterns that are unexpected with respect to prior knowledge and previously discovered patterns. The result of this technique is a small diverse set of understandable rules that characterise a specified property of interest. As another contribution this article derives a simple connection between subgroup discovery and classifier induction. For a popular utility function this connection allows to apply any standard rule induction algorithm to the task of subgroup discovery after a step of stratified resampling. The proposed techniques are empirically compared to state of the art subgroup discovery algorithms.

Pattern mining is considered as a really interesting task for the extraction of hidden knowledge in the form of patterns. The extraction of such subsequences, substructures or itemsets that represent any type of homogeneity and regularity in data has been carried out from unlabeled data. However, there are many research areas that aim at discovering patterns in the form of rules induced from labeled data. Hence, it is interesting to discover patterns and associations from a supervised point of view since a single item or a set of them can be considered as distinctive. This task, which is known as supervised local pattern mining, is described in this chapter, including different areas such as contrast set mining, emerging pattern mining, and subgroup discovery. This chapter is mainly focused on the subgroup discovery task, which is widely known in the field of supervised local pattern mining. Here, an exhaustive description about this task is provided, including some important quality measures in the field. Then, this chapter includes different evolutionary approaches for subgroup discovery. Finally, this chapter includes an analysis of other different approaches proposed for mining and quantifying local patterns.

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For the task of subgroup discovery, generalization-aware interesting measures that are based not only on the statistics of the patterns itself, but also on the statistics of their generalizations have recently been shown to be essential. A key technique to increase runtime performance of subgroup discovery algorithms is the application of optimistic estimates to limit the search space size. These are upper bounds for the interestingness that any specialization of the currently evaluated pattern may have. Until now these estimates are based on the anti-monotonicity of instances, which are covered by the current pattern. This neglects important properties of generalizations. Therefore, we present in this paper a new scheme of deriving optimistic estimates for generalization aware subgroup discovery, which is based on the instances by which patterns differ in comparison to their generalizations. We show, how this technique can be applied for the most popular interestingness measures for binary as well as for numeric target concepts. The novel bounds are incorporated in an efficient algorithm, which outperforms previous methods by up to an order of magnitude.KeywordsAssociation RuleQuality FunctionOptimistic EstimateTarget ConceptPositive InstanceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Social interactions are prevalent in our lives. These can be observed, e. g., online using social media, however, also offline specifically using sensors. In such contexts, typically time-stamped interactions are recorded, which can also be inferred from real-time location of humans. Such interaction data can then be modeled as so-called social interaction networks. For their analysis, a variety of different approaches can be applied. A prominent research direction is then the detection of patterns describing specific subgroups with exceptional behavioral characteristics, given some measure of interest. In the standard case of plain graphs modeling the interaction networks, methods for identifying such subgroups mainly focus on structural characteristics of the network and/or the induced subgraph. For attributed social networks, then additional attributive information can be exploited. This paper proposes to focus on the dyadic structure of the attributed social interaction networks, thus enabling a compositional perspective for identifying interesting subgroup patterns. Specifically, we can then analyze spatio-temporal data modeled as attributed social interaction networks for identifying exceptional social behavior. The presented approach adapts local pattern mining using subgroup discovery to the dyadic setting, exploiting attribute information of the spatio-temporal attributed interaction networks. With this, specific characteristics of social interactions are considered, i. e., duration and frequency, for identifying subgroups capturing social behavior that deviates from the norm. For subgroup discovery, we propose according interestingness measures in the form of seven novel quality functions and discuss their properties. In our experimentation, we perform an evaluation demonstrating the efficacy of the presented approach using four real-world datasets on face-to-face interactions in academic conferencing as well as school playground contexts. Our results indicate that the proposed method returns interesting, meaningful, and valid findings and results.

In data mining, the process of data obtained from users history databases is called Web usage mining. The main benefits lie in the improvement of the design of Web applications for the final user. This paper presents the application of subgroup discovery (SD) algorithms based on evolutionary fuzzy systems (EFSs) to the data obtained in an e-commerce Web site of extra virgin olive oil sale called http://www.orolivesur.com. For this purpose, a brief description of the SD process (objectives, properties, quality measures) and EFSs is presented. A discussion about the results obtained will also be included, especially focusing on the interests of the designer team of the Web site, providing some guidelines for improving several aspects such as usability and user satisfaction.

CSM-SD: Methodology for contrast set mining through subgroup discovery

We present Viper, for Visual Pattern Explorer, an innovative, browser-based application for interactive pattern exploration, assisted by visualisation, recommendation, and algorithmic search. The target audience consists of domain experts who have access to data but not to –potentially expensive– data mining experts. The goal of the system is to enable the target audience to perform true exploratory data mining. That is, to discover interesting patterns from data, with a focus on subgroup discovery but also facilitating frequent itemset mining.KeywordsDomain ExpertTarget AudiencePattern MiningVisual PatternInterestingness MeasureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Fuzzy rules generation is known an important task in designing fuzzy systems. This article applies an evolutionary fuzzy rules learning scheme to approach desired fuzzy systems having a lower fuzzy rules. The proposed learning scheme overcomes limitations of conventional fuzzy rules generation and completes the complex searching problems to extract the desired fuzzy system. In this article, aggregations of hyper-ellipsoids fuzzy partitions with different sizes and different positions are suggested to approximate the knowledge rule base of fuzzy systems whose membership functions are arbitrarily shaped and flexibly tuned in parameters searching space. Several corresponding parameters in defining the region of such hyper-ellipsoids type membership functions are efficiently selected based on the simple rule extracting technology. Furthermore, the constructed fuzzy system with only two fuzzy rules can be automatically extracted by the evolutional genetic algorithms (GAs) learning scheme with the guide of special fitness function. Finally, both inverted pendulum balance and nonlinear modeling problems are used to illustrate the effectiveness of the proposed method.

This paper presents a new operator called adaptive operator for the Pseudo-Bacterial Genetic Algorithm (PBGA). The PBGA was proposed by the authors as a new approach combining a genetic algorithm (GA) with a local improvement mechanism inspired by a process in bacterial genetics. The PBGA was applied for the discovery of fuzzy rules. The aim of the newly introduced adaptive operator is to improve the quality of the generated fuzzy rules, producing blocks of effective rules and more compact rule bases. The new operator adaptively decides the division points of each chromosome for the bacterial mutation and the cutting points for the crossover. In order to verify the efficiency of the proposed adaptive operator, the PBGA is applied to a simple fuzzy modeling problem. The new operator actuates according to the distribution of degrees of truth values of the rules. The results show the benefits that can be obtained with this operator.