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

Evolving temporal fuzzy association rules from quantitative data with a multi-objective evolutionary algorithm

A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifies numerous temporal association rules that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. The novelty of this research lies in exploring the composition of quantitative and temporal fuzzy association rules and the approach of using a hybridisation of a multi-objective evolutionary algorithm with fuzzy sets. Results show the ability of a multi-objective evolutionary algorithm (NSGA-II) to evolve multiple target itemsets that have been augmented into synthetic datasets

Mining Multidimensional Fuzzy Association Rules from a Normalized Database

Mining association rules is one of the important tasks in the process of data mining application. In general, the input as used in the process of generating rules is taken from a certain data table by which all the corresponding values of every domain data have correlations one to each others as given in the data table. A problem arises when we need to generate the rules expressing the relationship between two or more domains that belong to several different tables in a normalized database. To overcome the problem, before generating rules it is necessary to join the participant tables into a general table by a process called Denormalization. This paper shows a process of mining Multidimensional Fuzzy Association Rules from a normalized database. The process consists of two sub process, namely sub-process of join tables (Denormalization) and sub-process of mining fuzzy rules. In general, some parts of mining the fuzzy association rules has been discussed in our previous papers [3,4,5,6]

A fuzzy approach for mining quantitative association rules

During the last ten years, data mining, also known as knowledge discovery in databases, has established its position as a prominent and important research area. Mining association rules is one of the important research problems in data mining. Many algorithms have been proposed to find association rules in databases with quantitative attributes. The algorithms usually discretize the attribute domains into sharp intervals, and then apply simpler algorithms developed for boolean attributes. An example of a quantitative association rule might be "10% of married people between age 50 and 70 have at least 2 cars". Recently, fuzzy sets were suggested to represent intervals with non-sharp boundaries. Using the fuzzy concept, the above example could be rephrased e.g. "10% of married old people have several cars". However, if the fuzzy sets are not well chosen, anomalies may occur. In this paper we tackle this problem by introducing an additional fuzzy normalization process. Then we present the definition of quantitative association rules based on fuzzy set theory and propose a new algorithm for mining fuzzy association rules. The algorithm uses generalized definitions for interest measures. Experimental results show the efficiency of the algorithm for large databases

Multidimensional Time Series Fuzzy Association Rules Mining

In this paper, we present a new solution, in which the fuzziness of both subsequences and subsequences interval has been taken into consideration for solving the problem of multidimensional time series fuzzy association rules mining. Aimed at dealing with the new conception, this paper has put forward some key algorithms of the solution. Finally, an application example of multidimensional time series fuzzy association rules mining is illustrated. The result shows that rules with fuzzy interval can only be mined out by the above-mentioned new method

A Mining Algorithm under Fuzzy Taxonomic Structures

Most conventional data-mining algorithms identify the relationships among transactions using binary values and find rules at a single concept level. Transactions with quantitative values and items with taxonomic relations are, however, commonly seen in real-world applications. Besides, the taxonomic structures may also be represented in a fuzzy way. This paper thus proposes a fuzzy multiple-level mining algorithm for extracting fuzzy association rules under given fuzzy taxonomic structures. The proposed algorithm adopts a top-down progressively deepening approach to finding large itemsets. It integrates fuzzy-set concepts, data-mining technologies and multiple-level fuzzy taxonomy to find fuzzy association rules from given transaction data sets. Each item uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of the original items. The algorithm therefore focuses on the most important linguistic terms for reduced time complexit

Ассоциативные правила в интеллектуальном анализе данных

Рассмотрена задача построения моделей на основе ассоциативных правил. Проанализирован процесс поиска ассоциативных правил. Исследованы различные виды ассоциативных правил (негативные, численные, обобщенные, временные и нечеткие ассоциативные правила при использовании их для решения задач интеллектуального анализа данныхThe problem of synthesis of models based on association rules is concidered. The process of mining association rules is analyzed. Various types of association rules (negative, quantitative, generalized, temporal and fuzzy association rules) for solving data mining problems are investigate

Improve efficiency of fuzzy association rule using hedge algebra approach

A major problem when conducting mining fuzzy association rules from the database (DB) is the large computation time and memory needed. In addition, the selection of fuzzy sets for each attribute of the database is very important because it will affect the quality of the mining rule. This paper proposes a method for mining fuzzy association rules using the compressed database. We also use the approach of Hedge Algebra (HA) to build the membership function for attributes instead of using the normal way of fuzzy set theory. This approach allows us to explore fuzzy association rules through a relatively simple algorithm which is faster in terms of time, but it still brings association rules which are as good as the classical algorithms for mining association rules

MINING MULTIDIMENSIONAL FUZZY ASSOCIATION RULES FROM A DATABASE OF MEDICAL RECORD PATIENTS

Mining association rules is one of the important tasks in the process of data mining application. In general, the input as used in the process of generating rules is taken from a certain data table by which all the corresponding values of every domain data have correlations one to each others as given in the table. A problem arises when we need to generate the rules expressing the relationship between two or more domains that belong to several different tables in a normalized database. To overcome the problem, before generating rules it is necessary to join the participant tables into a general table by a process called Denormalization Process. This paper shows a process of generating Multidimensional Fuzzy Association Rules mining from a normalized database of medical record patients. The process consists of two sub-processes, namely sub-process of join tables (Denormalization Process) and sub-process of generating fuzzy rules. In general, the process of generating the fuzzy rules has been discussed in our previous papers [1, 2, 3, 4]. In addition to the process of generating fuzzy rules, this paper proposes a correlation measure of the rules as an additional consideration for evaluating interestingness of provided rules