Integrating computer log files for process mining: a genetic algorithm inspired technique

Process mining techniques are applied to single computer log files. But many processes are supported by different software tools and are by consequence recorded into multiple log files. Therefore it would be interesting to find a way to automatically combine such a set of log files for one process. In this paper we describe a technique for merging log files based on a genetic algorithm. We show with a generated test case that this technique works and we give an extended overview of which research is needed to optimise and validate this technique

Mining frequent biological sequences based on bitmap without candidate sequence generation

Biological sequences carry a lot of important genetic information of organisms. Furthermore, there is an inheritance law related to protein function and structure which is useful for applications such as disease prediction. Frequent sequence mining is a core technique for association rule discovery, but existing algorithms suffer from low efficiency or poor error rate because biological sequences differ from general sequences with more characteristics. In this paper, an algorithm for mining Frequent Biological Sequence based on Bitmap, FBSB, is proposed. FBSB uses bitmaps as the simple data structure and transforms each row into a quicksort list QS-list for sequence growth. For the continuity and accuracy requirement of biological sequence mining, tested sequences used during the mining process of FBSB are real ones instead of generated candidates, and all the frequent sequences can be mined without any errors. Comparing with other algorithms, the experimental results show that FBSB can achieve a better performance on both run time and scalability

Process equivalence in the context of genetic mining

In various application domains there is a desire to compare process models, e.g., to relate an organization-specific process model to a reference model, to find a web service matching some desired service description, or to compare some normative process model with a process model discovered using process mining techniques. Although many researchers have worked on different notions of equivalence (e.g., trace equivalence, bisimulation, branching bisimulation, etc.), most of the existing notions are not very useful in this context. First of all, most equivalence notions result in a binary answer (i.e., two processes are equivalent or not). This is not very helpful, because, in real-life applications, one needs to differentiate between slightly different models and completely different models. Second, not all parts of a process model are equally important. There may be parts of the process model that are rarely activated (i.e., "process veins") while other parts are executed for most process instances (i.e., the "process arteries"). Clearly, differences in some veins of a process are less important than differences in the main artery of a process. To address the problem, this paper proposes a completely new way of comparing process models. Rather than directly comparing two models, the process models are compared with respect to some typical behavior. This way, we are able to avoid the two problems just mentioned. The approach has been implemented and has been used in the context of genetic process mining. Although the results are presented in the context of Petri nets, the approach can be applied to any process modeling language with executable semantics

Identification of Patterns in Genetic-Algorithm-Based Solutions for Optimization of Process-Planning Problems Using a Data Mining Tool

This paper presents a novel use of data mining algorithms for extraction of knowledge from a set of process plans. The purpose of this paper is to apply data mining methodologies to explore the patterns in data generated by genetic-algorithm-generating process plans and to develop a rule set planner, which helps to make decisions in odd circumstances. Genetic algorithms are random-search algorithms based on the mechanics of genetics and natural selection. Because of genetic inheritance, the characteristics of the survivors after several generations should be similar. The solutions of a genetic algorithms for process planning consists of the operation sequence of a job, the machine on which each operation is performed, the tool used for performing each operation, and the tool approach direction. Among the optimal or near-optimal solutions, similar relationships may exist between the characteristics of the operation and sequential order. Data mining software known as See5 has been used to explore the relationship between the operation’s sequence and its attributes, and a set of rules has been developed. These rules can predict the positions of operations in the sequence of process planning

Data Mining and Hypothesis Refinement Using a Multi-Tiered Genetic Algorithm

This is the published version. Copyright De GruyterThis paper details a novel data mining technique that combines set objects with an enhanced genetic algorithm. By performing direct manipulation of sets, the encoding process used in genetic algorithms can be eliminated. The sets are used, manipulated, mutated, and combined, until a solution is reached. The contributions of this paper are two-fold: the development of a multi-tiered genetic algorithm technique, and its ability to perform not only data mining but also hypothesis refinement. The multi-tiered genetic algorithm is not only a closer approximation to genetics in the natural world, but also a method for combining the two main approaches for genetic algorithms in data mining, namely, the Pittsburg and Michigan approaches. These approaches were combined, and implemented. The experimental results showed that the developed system can be a successful data mining tool. More important, testing the hypothesis refinement capability of this approach illustrated that it could take a data model generated by some other technique and improves upon the overall performance of the data model

Mining Interesting Positive and Negative Association Rule Based on Improved Genetic Algorithm (MIPNAR_GA)

Association Rule mining is very efficient technique for finding strong relation between correlated data. The correlation of data gives meaning full extraction process. For the mining of positive and negative rules, a variety of algorithms are used such as Apriori algorithm and tree based algorithm. A number of algorithms are wonder performance but produce large number of negative association rule and also suffered from multi-scan problem. The idea of this paper is to eliminate these problems and reduce large number of negative rules. Hence we proposed an improved approach to mine interesting positive and negative rules based on genetic and MLMS algorithm. In this method we used a multi-level multiple support of data table as 0 and 1. The divided process reduces the scanning time of database. The proposed algorithm is a combination of MLMS and genetic algorithm. This paper proposed a new algorithm (MIPNAR_GA) for mining interesting positive and negative rule from frequent and infrequent pattern sets. The algorithm is accomplished in to three phases: a).Extract frequent and infrequent pattern sets by using apriori method b).Efficiently generate positive and negative rule. c).Prune redundant rule by applying interesting measures. The process of rule optimization is performed by genetic algorithm and for evaluation of algorithm conducted the real world dataset such as heart disease data and some standard data used from UCI machine learning repository.Keywords— Association rule mining, negative rule and positive rules, frequent and infrequent pattern set, genetic algorithm

Study of Genetic Algorithm, an Evolutionary Approach

Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures, from large amount of data stored in databases, data warehouses, or other information repositories. To do this process, data mining uses a variety of algorithms according to the specifications of measures and threshold. The results of this analysis are then used to build models based on real world behavior, which are in turn used to analyze incoming data and make predictions about future behavior. Here, we are focusing on one of the efficient evolutionary algorithm called genetic algorithm. This is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. Genetic algorithms are numerical optimization algorithms inspired by both natural selection and natural genetics. This method is a general one, capable of being applied to an extremely wide range of problems. In this paper we will discuss the Genetic algorithm techniques and its application in data mining in detail

EVOLUTIONARY ALGORITHMS LEARNING METHODS IN STUDENT EDUCATION

Teaching experience shows that during educational process student perceive graphical information better than analytical relationships. As a possible solution, there could be the use of package Matlab in realization of different algorithms for IT studies. Students are very interested in modern data mining methods, such as artificial neural networks, fuzzy logic, clustering and evolution methods. Series of research were carried out in order to demonstrate the suitability of the Matlab for the purpose of visualization of various simulation models of some data mining disciplines – particularly genetic algorithms. Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. There are four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyses present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. Genetic algorithm learning methods are often undeservedly forgotten, although the implementation of their algorithms is relatively strong and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies were demonstrated based on genetic algorithms and real examples. We assume that students already have prior knowledge of genetic algorithms.