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

Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty

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

Apriori algorithm on Marine Fisheries Biological Data

Abstract - Data Mining (DM) is the process of analysing data from different vista and gives summary on specific determination. Association rules are rules describing the associations or correlations to bring out the hidden pattern among attributes in data sets. The most widely used algorithm in association technique is Apriori algorithm which is meant for only categorical data analysis. The sample fishery biological data consist of six attributes out of which two are numerical values. As a new attempt, the numerical values were converted to unique nominal values in order to maintain all categorical values. The Apriori algorithm applied on specific criteria such as minimum support and confidence enabled to derive many meaningful patterns on different perspectives. The taeniopterus apecies has more associations between the attributes of total_length range between 120 to 150 and month of August, weight of Thirty and sex of Male

Discovery of association rules from medical data -classical and evolutionary approaches

The paper presents a method of association rules discovering from medical data using the evolutionary approach. The elaborated method (EGAR) uses a genetic algorithm as a tool of knowledge discovering from a set of data, in the form of association rules. The method is compared with known and common method - FPTree. The developed computer program is applied for testing the proposed method and comparing the results with those produced by FPTree. The program is the general and flexible tool for the rules generation task using different data sets and two embodied methods. The presented experiments are performed using the actual medical data from the Wroclaw Clinic

Mining range associations for classification and characterization

In this paper, we propose a method that is able to derive rules involving range associations from numerical attributes, and to use such rules to build comprehensible classification and characterization (data summary) models. Our approach follows the classification association rule mining paradigm, where rules are generated in a way similar to association rule mining, but search is guided by rule consequents. This allows many credible rules, not just some dominant rules, to be mined from the data to build models. In so doing, we propose several sub-range analysis and rule formation heuristics to deal with numerical attributes. Our experiments show that our method is able to derive range-based rules that offer both accurate classification and comprehensible characterization for numerical data

Interactive Search of Rules in Medical Data Using Multiobjective Evolutionary Algorithms

ABSTRACT In this work, we propose an approach for evolving rules from medical data based on an interactive multi-criteria evolutionary search: besides selecting the set of criteria and the sets of potential antecedent and consequent attributes, the user can also intervene in the searching process by marking the uninteresting rules. The marked rules are further used in estimating a supplementary optimization criterion which expresses the user's opinion on the rule quality and is taken into account in the evolutionary process

An evolutionary algorithm to discover numeric association rules

Association rules are one of the most used tools to discover relationships among attributes in a database. Nowadays, there are many efficient techniques to obtain these rules, although most of them require that the values of the attributes be discrete. To solve this problem, these techniques discretize the numeric attributes, but this implies a loss of information. In a general way, these techniques work in two phases: in the first one they try to find the sets of attributes that are, with a determined frequency, within the database (frequent itemsets), and in the second one, they extract the association rules departing from these sets. In this paper we present a technique to find the frequent itemsets in numeric databases without needing to discretize the attributes. We use an evolutionary algorithm to find the intervals of each attribute that conforms a frequent itemset. The evaluation function itself will be the one that decide the amplitude of these intervals. Finally, we evaluate the tool with synthetic and real databases to check the efficiency of our algorithm

An Evolutionary Algorithm to Discover Numeric Association Rules

Association rules are one of the most used tools to discover relationships among attributes in a database. Nowadays, there are many e#cient techniques to obtain these rules, although most of them require that the values of the attributes be discrete. To solve this problem, these techniques discretize the numeric attributes, but this implies a loss of information. In a general way, these techniques work in two phases: in the first one they try to find the sets of attributes that are, with a determined frequency, within the database (frequent itemsets), and in the second one, they extract the association rules departing from these sets. In this paper we present a technique to find the frequent itemsets in numeric databases without needing to discretize the attributes. We use an evolutionary algorithm to find the intervals of each attribute that conforms a frequent itemset. The evaluation function itself will be the one that decide the amplitude of these intervals. Finally, we evaluate the tool with synthetic and real databases to check the e#ciency of our algorithm