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

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

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

Encapsulation of Soft Computing Approaches within Itemset Mining a A Survey

Data Mining discovers patterns and trends by extracting knowledge from large databases. Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc. aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide efficient search algorithms for selecting a model, from mixed media data. Data mining refers to information extraction while soft computing is used for information processing. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. This survey paper explores the usage of soft computing approaches in itemset utility mining

A Review on: Efficient Method for Mining Frequent Itemsets on Temporal Data

Temporal data can hold time-stamped information that affects the results of data mining. Customary strategies for finding frequent itemsets accept that datasets are static; also the instigated rules are relevant over the whole dataset. In any case, this is not the situation when data is temporal. The work is done to enhance the proficiency of mining frequent itemsets on temporal data. The patterns can hold in either all or, then again a portion of the intervals. It proposes another method with respect to time interval is called as frequent itemsets mining with time cubes. The concentration is building up an efficient algorithm for this mining issue by broadening the notable a priori algorithm. The thought of time cubes is proposed to handle different time hierarchies. This is the route by which the patterns that happen intermittently, amid a time interval or both, are perceived. Another thickness limit is likewise proposed to take care of the overestimating issue of time periods and furthermore ensure that found patterns are valid

A Review on: Efficient Method for Mining Frequent Itemsets on Temporal Data

Temporal data can hold time-stamped information that affects the results of data mining. Customary strategies for finding frequent itemsets accept that datasets are static; also the instigated rules are relevant over the whole dataset. In any case, this is not the situation when data is temporal. The work is done to enhance the proficiency of mining frequent itemsets on temporal data. The patterns can hold in either all or, then again a portion of the intervals. It proposes another method with respect to time interval is called as frequent itemsets mining with time cubes. The concentration is building up an efficient algorithm for this mining issue by broadening the notable a priori algorithm. The thought of time cubes is proposed to handle different time hierarchies. This is the route by which the patterns that happen intermittently, amid a time interval or both, are perceived. Another thickness limit is likewise proposed to take care of the overestimating issue of time periods and furthermore ensure that found patterns are valid

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment