Web Usage Mining with Evolutionary Extraction of Temporal Fuzzy Association Rules

In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets' boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules. (C) 2013 Elsevier B.V. All rights reserved

Web Usage Mining to Extract Knowledge for Modelling Users of Taiwan Travel Recommendation Mobile APP

This work presents the design of a web mining system to understand the navigational behavior of passengers in developed Taiwan travel recommendation mobile app that provides four main functions including recommend by location , hot topic , nearby scenic spots information , my favorite and 2650 scenic spots. To understand passenger navigational patterns, log data from actual cases of app were collected and analysed by web mining system. This system analysed 58981 sessions of 1326 users for the month of June, 2014. Sequential profiles for passenger navigational patterns were captured by applying sequence-based representation schemes in association with Markov models and enhanced K-mean clustering algorithms for sequence behavior mining cluster patterns. The navigational cycle, time, function numbers, and the depth and extent (range) of app were statistically analysed. The analysis results can be used improved the passengers\u27 acceptance of app and help generate potential personalization recommendations for achieving an intelligent travel recommendation service

Subgroup Discovery trhough Evolutionary Fuzzy Systems applied to Bioinformatic problems

Subgroup discovery is a descriptive data mining technique using supervised learning. This paper presents a summary about the main properties and elements about subgroup discovery task. In addition, we will focus on the suitability and potential of the search performed by evolutionary algorithms in order to apply in the development of subgroup discovery algorithms, and in the use of fuzzy logic which is a soft computing technique very close to the human reasoning. The hybridisation of both techniques are well known as evolutionary fuzzy system. The most relevant applications of evolutionary fuzzy systems for subgroup discovery in the bioinformatics domains are outlined in this work. Specifically, these algorithms are applied to a problem based on the Influenza A virus and the accute sore throat problem

Supervised Descriptive Rule Discovery: A Survey of the State-of-the-Art

The supervised descriptive rule discovery concept groups a set of data mining techniques whose objective is to describe data with respect to a property of interest. Among the techniques within this concept are the subgroup discovery, emerging patterns and contrast sets. This contribution presents the supervised descriptive rule discovery concept within the data mining literature. Specifically, it is important to remark the main di erence with respect to other existing techniques within classification or description. In addition, a a survey of the state-of-the-art about the different techniques within supervised descriptive rule discovery throughout the literature can be observed. The paper allows to the experts to analyse the compatibilities between terms and heuristics of the different data mining tasks within this concept

Web usage analysis of Pillar 3 disclosed information by deposit customers in turbulent times

Market discipline has been a scrutinized area since the last financial crisis in 2008. Regulators strengthened their role particularly through Pillar 3 in Basel III. However, there are still some aspects of market discipline that deserve special attention to avoid future failures. This study focuses on the analysis of the interest and behaviour of deposit stakeholders based on website data dedicated to disclosures of commercial bank in Slovakia during and after turbulent times (period 2009–2012). The data consists of log files, and web mining techniques were applied (the modelling of web user behaviour in dependence on time - based on the proposals of the authors). The results show that also in turbulent times, stakeholders’ interest in Pillar 3 disclosures is low (in line with (Munk, Pilkova, Benko, & Blažeková, 2017)) and the highest interest was identified for the Pricing List category. After turbulent times, Pillar 3 categories (Pillar 3 related information and Pillar 3 disclosures) have weak interest, with peaks at the beginning of the year, and the highest increase was in the Business Conditions category. The results suggest that the enhancement of interest of key stakeholders in disclosures inevitably requires changes to deliver sufficient disclosure data structures and to design a disclosure policy that fulfils regulatory expectations. © 2021 The AuthorsScientific Grant Agency of the Ministry of Education of the Slovak Republic (ME SR); Slovak Academy of Sciences (SAS) [VEGA-1/0776/18, VEGA-1/0821/21]Slovenská Akadémia Vied, SAV: VEGA-1/0776/18, VEGA-1/0821/2

Learning lost temporal fuzzy association rules

Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are inadequate. The Genetic Iterative Temporal Fuzzy Association Rule Mining (GITFARM) framework solves the problem by utilising flexible fuzzy representations from a fuzzy rule-based system (FRBS). The combination of temporal, fuzzy and itemset space was simultaneously searched with a genetic algorithm (GA) to overcome the problem. The framework transforms the dataset to a graph for efficiently searching the dataset. A choice of model in fuzzy representation provides a trade-off in usage between an approximate and descriptive model. A method for verifying the solution to the hypothesised problem was presented. The proposed GA-based solution was compared with a traditional approach that uses an exhaustive search method. It was shown how the GA-based solution discovered rules that the traditional approach did not. This shows that simultaneously searching for rules and membership functions with a GA is a suitable solution for mining temporal fuzzy association rules. So, in practice, more knowledge can be discovered for making well-informed decisions that would otherwise be lost with a traditional approach.EPSRC DT

Minería de datos mediante programación automática con colonias de hormigas

La presente tesis doctoral supone el primer acercamiento de la metaheur stica de programaci on autom atica mediante colonias de hormigas (Ant Programming) a tareas de miner a de datos. Esta t ecnica de aprendizaje autom atico ha demostrado ser capaz de obtener buenos resultados en problemas de optimizaci on, pero su aplicaci on a la miner a de datos no hab a sido explorada hasta el momento. Espec camente, esta tesis cubre las tareas de clasi caci on y asociaci on. Para la primera se presentan tres modelos que inducen un clasi cador basado en reglas. Dos de ellos abordan el problema de clasi caci on desde el punto de vista de evaluaci on monobjetivo y multiobjetivo, respectivamente, mientras que el tercero afronta el problema espec co de clasi caci on en conjuntos de datos no balanceados desde una perspectiva multiobjetivo. Por su parte, para la tarea de extracci on de reglas de asociaci on se han desarrollado dos algoritmos que llevan a cabo la extracci on de patrones frecuentes. El primero de ellos propone una evaluaci on de los individuos novedosa, mientras que el segundo lo hace desde un punto de vista basado en la dominancia de Pareto. Todos los algoritmos han sido evaluados en un marco experimental adecuado, utilizando numerosos conjuntos de datos y comparando su rendimiento frente a otros m etodos ya publicados de contrastada calidad. Los resultados obtenidos, que han sido veri cados mediante la aplicaci on de test estad sticos no param etricos, demuestran los bene cios de utilizar la metaheur stica de programaci on autom atica con colonias de hormigas para dichas tareas de miner a de datos.This Doctoral Thesis involves the rst approximation of the ant programming metaheuristic to data mining. This automatic programming technique has demonstrated good results in optimization problems, but its application to data mining has not been explored until the present moment. Speci cally, this Thesis deals with the classi cation and association rule mining tasks of data mining. For the former, three models for the induction of rule-based classi ers are presented. Two of them address the classi cation problem from the point of view of single-objective and multi-objective evaluation, respectively, while the third proposal tackles the particular problem of imbalanced classi cation from a multi-objective perspective. On the other hand, for the task of association rule mining two algorithms for extracting frequent patterns have been developed. The rst one evaluates the quality of individuals by using a novel tness function, while the second algorithm performs the evaluation from a Pareto dominance point of view. All the algorithms proposed in this Thesis have been evaluated in a proper experimental framework, using a large number of data sets and comparing their performance against other published methods of proved quality. The results obtained have been veri ed by applying non-parametric statistical tests, demonstrating the bene ts of using the ant programming metaheuristic to address these data mining tasks