Temporal fuzzy association rule mining with 2-tuple linguistic representation

This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

Binary Particle Swarm Optimization based Biclustering of Web usage Data

Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms

Unfolding-Based Process Discovery

This paper presents a novel technique for process discovery. In contrast to the current trend, which only considers an event log for discovering a process model, we assume two additional inputs: an independence relation on the set of logged activities, and a collection of negative traces. After deriving an intermediate net unfolding from them, we perform a controlled folding giving rise to a Petri net which contains both the input log and all independence-equivalent traces arising from it. Remarkably, the derived Petri net cannot execute any trace from the negative collection. The entire chain of transformations is fully automated. A tool has been developed and experimental results are provided that witness the significance of the contribution of this paper.Comment: This is the unabridged version of a paper with the same title appearead at the proceedings of ATVA 201

Verslo procesų prognozavimo ir imitavimo taikant sisteminių įvykių žurnalų analizės metodus tyrimas

Business process (BP) analysis is one of the core activities in organisations that lead to improvements and achievement of a competitive edge. BP modelling and simulation are one of the most widely applied methods for analysing and improving BPs. The analysis requires to model BP and to apply analysis techniques to the models to answer queries leading to improvements. The input of the analysis process is BP models. The models can be in the form of BP models using industry-accepted BP modelling languages, mathematical models, simulation models and others. The model creation is the most important part of the BP analysis, and it is both time-consuming and costly activity. Nowadays most of the data generated in the organisations are electronic. Therefore, the re-use of such data can improve the results of the analysis. Thus, the main goal of the thesis is to improve BP analysis and simulation by proposing a method to discover a BP model from an event log and automate simulation model generation. The dissertation consists of an introduction, three main chapters and general conclusions. The first chapter discusses BP analysis methods. In addition, the process mining research area is presented, the techniques for automated model discovery, model validation and execution prediction are analysed. The second part of the chapter investigates the area of BP simula-tion. The second chapter of the dissertation presents a novel method which automatically discovers Bayesian Belief Network from an event log and, furthermore, automatically generates BP simulation model. The discovery of the Bayesian Belief Network consists of three steps: the discovery of a directed acyclic graph, generation of conditional probability tables and their combination. The BP simulation model is generated from the discovered directed acyclic graph and uses the belief network inferences during the simulation to infer the execution of the BP and to generate activity data dur-ing the simulation. The third chapter presents the experimental research of the proposed network and discusses the validity of the research and experiments. The experiments use selected logs that exhibit a wide array of behaviour. The experiments are performed in order to test the discovery of the graphs, the inference of the current process instance execution probability, the predic-tion of the future execution of the process instances and the correctness of the simulation. The results of the dissertation were published in 9 scientific publica-tions, 2 of which were in reviewed scientific journals indexed in Clarivate Analytics Science Citation Index

Mining complete, precise and simple process models

Process discovery algorithms are generally used to discover the underlying process that has been followed to achieve an objective. In general, these algorithms do not take into account any domain knowledge to derive process models, allowing to apply them in a general manner. However, depending on the selected approach, a different kind of process models can be discovered, as each technique has its strengths and weaknesses, e.g., the expressiveness of the used notation. Hence, it is important to take into account the requirements of the domain when deciding which algorithm to use, as the correct assumptions can lead to richer process models. For instance, among the different domains of application of process mining we can identify several fields that share an interesting requirement about the discovered process models. In security audits, discovered processes have to fulfill strict requisites. This means that the process model should reproduce as much behavior as possible; otherwise some violations may go undetected (replay fitness). On the other hand, in order to avoid false positives, process models should reproduce only the recorded behavior (precision). Finally, process models should be easily readable to better detect deviations (simplicity). Another clear example concerns the educational domain, as in order to be of value for both teachers and learners, a discovered learning process should satisfy the aforementioned requirements. That is, to guarantee feasible and correct evaluations, teachers need to access to all the activities performed by learners, thereby the learning process should be able to reproduce as much behavior as possible (replay fitness). Furthermore, the learning process should focus on the recorded behavior seen in the event log (precision), i.e., show only what the students did, and not what they might have done, while being easily interpretable by the teachers (simplicity). One of the previous requirements is related to the readability of process models: simplicity. In process mining, one of the identified challenges is the appropriate visualization of process models, i.e., to present the results of process discovery in such a way that people actually gain insights about the process. Process models that are unnecessary complex can hinder the real behavior of the process rather than to provide an intuition of what is really happening in an organization. However, achieving a good level of readability is not always straightforward, for instance, due the used representation. Within the different approaches focused to reduce the complexity of a process model, the interest in this PhD Thesis relies on two techniques. On the one hand, to improve the readability of an already discovered process model through the inclusion of duplicate labels. On the other hand, the hierarchization of a process model, i.e., to provide a well known structure to the process model. However, regarding the latter, this technique requires to take into account domain knowledge, as different domains may rely on different requirements when improving the readability of the process model. In other words, in order to improve the interpretability and understandability of a process model, the hierarchization has to be driven by the domain. To sum up, concerning the aim of this PhD Thesis, we can identify two main topics of interest. On the one hand, we are interested in retrieving process models that reproduce as much behavior recorded in the log as possible, without introducing unseen behavior. On the other hand, we try to reduce the complexity of the mined models in order to improve their readability. Hence, the aim of this PhD Thesis is to discover process models considering replay fitness, precision and simplicity, while paying special attention in retrieving highly interpretable process models