Efficient Generation of Association Rules from Numeric Data Using Genetic Algorithm for Smart Cities

In this paper we deal with association rule mining in the context of a complex, interactive and iterative knowledge discovery process. After a general introduction covering the basics of association rule mining and of the knowledge discovery process in databases we draw the attention to the problematic aspects concerning the integration of both. Actually, we come to the conclusion that with regard to human involvement and interactivity the current situation is far from being satisfying. In our paper we tackle this problem on three sides: First of all there is the algorithmic complexity. Although today’s algorithms efficiently prune the immense search space the achieved run times do not allow true interactivity. Nevertheless we present a rule caching schema that significantly reduces the number of mining runs. This schema helps to gain interactivity even in the presence of extreme run times of the mining algorithms. Second, today the mining data is typically stored in a relational database management system. We present an efficient integration with modern database systems which is one of the key factors in practical mining applications. Third, interesting rules must be picked from the set of generated rules. This might be quite costly because the generated rule sets normally are quite large whereas the percentage of useful rules is typically only a very small fraction. We enhance the traditional association rule mining framework in order to cope with this situation.KeywordsAssociation RuleMining AlgorithmFrequent ItemsetsAssociation Rule MiningGeneralize Association RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

WITHDRAWN: Combined approach for mining high quality interesting association rules

Data mining technology has emerged as a means for identifying patterns and trends from large quantities of data. Mining encompasses various algorithms such as clustering, classification, and association rule mining. In this paper we take advantage of the genetic algorithm (GA) designed specifically for discovering association rules. We propose a novel spatial mining algorithm, called ARMNGA(association rules mining in novel genetic algorithm), Compared to the algorithm in [Agrawal R, et al., Mining association rules between sets of items in large databases, 1993], the ARMNGA algorithm avoids generating impossible candidates, and therefore is more efficient in terms of the execution time.

Association rule mining is an important approach to data mining. It extracts useful and hidden information. There are two methodologies to explore the association rules. One method is generating frequent pattern generation through apriori like algorithms whereas another methodology is by using the soft computing techniques especially genetic algorithm. Two important aspect which is most of the time unaddressed, is incremental data and multi-objective. Very few research work on incremental and multi-objective association rule mining has been done. This paper comprises of a comprehensive study of incremental data mining and a distinct study of genetic algorithms. It is observed that soft-computing technique perform better for association rules. There is also a need for Incremental algorithms which work better in the state of addition, deletion and modification of data. It is also found that strong need of Multi-objective Incremental association rule mining algorithm.

Most association rule mining techniques concentrate on finding frequent rules. However, rare association rules are in some cases more interesting than frequent association rules since rare rules represent unexpected or unknown associations. All current algorithms for rare association rule mining use an Apriori level-wise approach which has computationally expensive candidate generation and pruning steps. We propose RP-Tree, a method for mining a subset of rare association rules using a tree structure, and an information gain component that helps to identify the more interesting association rules. Empirical evaluation using a range of real world datasets shows that RP-Tree itemset and rule generation is more time efficient than modified versions of FP-Growth and ARIMA, and discovers 92-100% of all the interesting rare association rules. Additional evaluation using synthetic datasets also shows that RP-Tree is more efficient, in addtion to showing how the execution time of RP-Tree changes with transaction length and rare-item itemset size.

Multi-dimensional semantic clustering of large databases for association rule mining

Data quality on categorical attribute is a difficult problem that has not receivedas much attention as numerical counterpart. Our basic idea is to employ association rule for the purpose of data quality measurement. Strong rule generation is an important area of data mining.Association rule mining problems can be considered as a multiobjective problem rather than as a single objective one.The main area of concentration was the rules generated by association rule mining using gene tic algorithm. The advantage of using geneticalgorithm is to discover high level predictionrules is that they perform a global search and cope better with attribute interaction than the greedy rule induction algorithm often used in data mining. Genetic algorithm based approach utilizes the linkage betweenassociation rule and feature selection. In this paper, we put forward a Multi objective genetic algorithm approach for data quality on categorical attributes. The result shows that our approach is outperformed by the objecti ves like accuracy, completeness, comprehensibilityand interestingness.

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.

Mining association rules is an important data mining technology aiming to find the relationship among items in the databases. Genetic-fuzzy data mining uses evolutionary algorithm, such as genetic algorithm (GA), to optimize the membership functions for mining fuzzy association rules, and has received considerable success. The increase in data, especially in big data analytics, poses serious challenges to GA in the effectiveness and efficiency of finding appropriate membership functions. This study proposes a GA for enhancing genetic-fuzzy mining of association rules. First, we design a novel chromosome representation considering the structures of membership functions. The representation facilitates arrangement of membership functions. Second, this study presents two heuristics in the light of overlap and coverage for removing inappropriate arrangement. A series of experiments is conducted to examine the proposed GA on different amounts of transactions. The experimental results show that GA benefits from the proposed representation. The two heuristics help to explore the structures of membership functions and achieve significant improvement on GA in terms of solution quality and convergence speed. The satisfactory outcomes validate the high capability of the proposed GA in genetic-fuzzy mining of association rules.

Due to the wide variety of 10kV distribution network equipment and the complex operating environment, various defects have emerged frequently. Because the distribution network is directly connected to users, the distribution network equipment defects will lead to the occurrence of distribution network failures if not processed in time, affecting the safe and stable operation of the power grid. In this paper, a genetic algorithm(GA)-based method is used to find useful association rules from the defect data set of distribution network equipment. In this method, genetic algorithm is used to avoid scanning transaction database multiple times and improve the efficiency of mining association rules. Moreover, the confidence factor is used to objectively measure the interest of the association rules without providing a minimum support threshold. In this paper, the defect association analysis of the main distribution equipment in a region is carried out, and the association results are obtained, which can help the maintenance personnel to find the family defects of the equipment and speculate the type of defects, providing support for the maintenance and repair of equipment.

To come over the limitations of Apriori algorithm and association rule mining algorithm based on Genetic Algorithm (GA), this paper proposed a new association rule mining algorithm based on the population-based incremental algorithm (PBIL), which is a kind of distribution estimation algorithms. The proposed association rule-mining algorithm keeps the advantages of GA mining association rules in coding and the fitness function. Through using probability vector possessing learning properties to update the population, the algorithm increases the convergence speed and enhances the searching ability, compared to GA. In the experiment of mining association, rules in blood indices data, PBIL algorithm performs better not only in running time, convergence speed, but also achieve better searching results. Meanwhile, this paper proposed a parallel algorithm for association rule mining based on PBIL and designed a system architecture based on cloud computing for blood indices analysis, providing a good example to apply the new algorithm to cloud computing.

This research demonstrates the application of association rule mining to spatio‐temporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A→B where A (the antecedent) and B (the consequent) are sets of predicates. A spatio‐temporal association rule occurs when there is a spatio‐temporal relationship in the antecedent or consequent of the rule. As a case study, association rule mining is used to explore the spatial and temporal relationships among a set of variables that characterize socioeconomic and land cover change in the Denver, Colorado, USA region from 1970–1990. Geographic Information Systems (GIS)‐based data pre‐processing is used to integrate diverse data sets, extract spatio‐temporal relationships, classify numeric data into ordinal categories, and encode spatio‐temporal relationship data in tabular format for use by conventional (non‐spatio‐temporal) association rule mining software. Multiple level association rule mining is supported by the development of a hierarchical classification scheme (concept hierarchy) for each variable. Further research in spatio‐temporal association rule mining should address issues of data integration, data classification, the representation and calculation of spatial relationships, and strategies for finding ‘interesting’ rules.

Association Rule Mining is one of the important data mining activities and has received substantial attention in the literature. Association rule mining is a computationally and I/O intensive task. In this paper, we propose a solution approach for mining optimized fuzzy association rules of different orders. We also propose an approach to define membership functions for all the continuous attributes in a database by using clustering techniques. Although single objective genetic algorithms are used extensively, they degenerate the solution. In our approach, extraction and optimization of fuzzy association rules are done together using multi-objective genetic algorithm by considering the objectives such as fuzzy support, fuzzy confidence and rule length. The effectiveness of the proposed approach is tested using computer activity dataset to analyze the performance of a multi processor system and network audit data to detect anomaly based intrusions. Experiments show that the proposed method is efficient in many scenarios.

In this developing paradigm, heterogeneous network infrastructure is combined with ubiquitous devices for sensing, smart control systems and big data analytics to create “smart cities.” Enhancing the overall quality of life is one of their main goals and they do so through smart services they offer in a broad range of areas such as transportation, healthcare, entertainment (including virtual reality), the environment (including pollution) and energy. To be able to offer those services, the job of big data and their analysis is complicated, since it allows for the extraction of useful insights from the enormous amounts of data that the smart cities produce. The current state-of-the-art research efforts aimed at big data analytics in a smart city setting are investigated and presented in this chapter. In particular, a comprehensive literature review on the big data analytic frameworks for smart cities is provided. Following that, a short talk on IoT and big data is briefed. Owing to different data that the smart cities collect, which frequently have contradictory processing needs, appropriate investigative methods based on the data type are also recommended, depending on the data type. The many smart city applications were investigated and a perspective on the difficulties associated with smart city development was developed. In this chapter, the methods addressing the smart city issues and development are examined, and the possibility for the employment of integrated IoT and big data analytics is presented. Finally, as a case study, a briefing on a call service application for the fire department in a smart city is provided to round up the presentation.

Association Rules Mining is an important data mining task that has many applications. Association rules mining is considered as an optimization problem; thus several metaheuristics have been developed to solve it since they have been proven to be faster than the exact algorithms. However, most of them generates a lot of redundant rules. In this work, we proposed a modified genetic algorithm for mining interesting non-redundant association rules. Different experiments have been carried out on several well-known benchmarks. Moreover, the algorithm was compared with those of other published works and the results found proved the efficiency of our proposal.KeywordsData miningAssociation rules miningMulti-objective association rules miningMetaheuristicsGenetic algorithmQuality measuresRedundancy