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Association rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.

The primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.

The experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.

Data mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

Nowadays, database management system has been applied in library management, and a great number of data about readers’ visiting history to resources have been accumulated by libraries. A lot of important information is concealed behind such data. The purpose of this paper is to use a typical data mining (DM) technology named an association rule mining model to find out borrowing rules of readers according to their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase utilization rate of data resources at library.

Association rule mining algorithm is applied to find out borrowing rules of readers according to their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase utilization rate of data resources at library.

Through an analysis on record of book borrowing by readers, library manager can recommend books that may be interested by a reader based on historical borrowing records or current book-borrowing records of the reader.

If many different categories of book-borrowing problems are involved, it will result in large length of encoding as well as giant searching space. Therefore, future research work may be considered in the following aspects: introduce clustering method; and apply association rule mining method to procurement of book resources and layout of books.

The paper provides a helpful inspiration for Big Data mining and software development, which will improve their efficiency and insight on users’ behavior and psychology.

The paper proposes a framework to help users understand others’ behavior, which will aid them better take part in group and community with more contribution and delightedness.

DM technology has been used to discover information concealed behind Big Data in library; the library personalized recommendation problem has been analyzed and formulated deeply; and a method of improved association rules combined with artificial bee colony algorithm has been presented.

The purpose of this paper is to propose an algorithm that is based on the ant colony optimization (ACO) metaheuristic for producing harmonized melodies. ACO is a nature inspired metaheuristic where a colony of ants searches for an optimum of a function. The algorithm works in two stages. In the first stage it creates a melody. The obtained melody is then harmonized according to the rules of baroque harmony in the second stage. A multi‐objective version of the algorithm is also proposed, where each tier is optimized as a separate objective.

The ACO metaheuristic is adapted to graphs representing notes and chords. Desirability of a sequence of notes is measured by conformance to compositional rules. The fitness of a melody is evaluated with five equally weighted rules governing smoothness of the melody curve, its contour, tendency tone resolution, tone colors and the pitch of the final note. Harmonization is guided by six rules, grouped into three tiers of two rules each. These rules cover chord arrangement, voice distance, voice leading, harmonic progression, smoothness, and chord resolution. Rules of a tier do not score unless those of the previous tier yield high values.

The proposed algorithm improves on the only other existing musical ACO by adding the notion of harmony and by evolving voices codependently. The output is comparable to different types of other existing algorithms (genetic algorithm, rule‐based search algorithm) in the field. The multi‐objective variant significantly enhances solution quality and convergence speed, which makes extensions of the system for real time performance realistic.

This algorithm is the first ACO algorithm proposed for the problem of melody creation and harmonization.

The purpose of this paper is to propose a way of implementing data mining (DM) techniques and algorithms to apply quality improvement (QI) approaches in order to resolve quality issues (Rokach and Maimon, 2006; Köksal et al., 2011; Kahraman and Yanik, 2016). The effectiveness of the proposed methodologies is demonstrated through their application results. The goal of this paper is to develop a DM system based on the seven new QI tools in order to discover useful knowledge, in the form of rules, that are hidden in a vast amount of data and to propose solutions and actions that will lead an organization to improve its quality through the evaluation of the results.

Four popular data-mining approaches (rough sets, association rules, classification rules and Bayesian networks) are applied on a set of 12,477 case records concerning vehicle damages. The set of rules and patterns that is produced by each algorithm is used as an input in order to dynamically form each of the seven new quality tools (QTs).

The proposed approach enables the creation of the QTs starting from the raw data and passing through the DM process.

The present paper proposes an innovative work concerning the formation of the seven new QTs of quality management using DM popular algorithms. The resulted seven DM QTs were used to identify patterns and understand, so they can lead even non-experts to draw useful conclusions and make decisions.

Conventional frequent itemsets mining ignores the fact that the relative benefits or significance of “transactions” belonging to different customers are different in most of the relevant applied studies, which leads to failure to obtain some association rules with lower support but from higher-value consumers. Because not all customers are financially attractive to firms, it is necessary that their values be determined and that transactions be weighted. The purpose of this study is to propose a novel consumer preference mining method based on conventional frequent itemsets mining, which can discover more rules from the high-value consumers.

In this study, the authors extend the conventional association rule problem by associating the “annual purchase amount” – “price preference” (AP) weight with a consumer to reflect the consumer’s contribution to a market. Furthermore, a novel consumer preference mining method, the AP-weclat algorithm, is proposed by introducing the AP weight into the weclat algorithm for discovering frequent itemsets with higher values.

The experimental results from the survey data revealed that compared with the weclat algorithm, the AP-weclat algorithm can make some association rules with low support but a large contribution to a market pass the screening by assigning different weights to consumers in the process of frequent itemsets generation. In addition, some valuable preference combinations can be provided for related practitioners to refer to.

This study is the first to introduce the AP-weclat algorithm for discovering frequent itemsets from transactions through considering AP weight. Moreover, the AP-weclat algorithm can be considered for application in other markets.

A learning algorithm called CLILP2 (Cover Learning Using Integer Linear Programming) is applied to medical data to generate rules to recognize patients with coronary artery disease. The algorithm partitions a data set into subsets using features which best describe and distinguish a particular subset from all other subsets. These features are used to form the rules which can be used as the knowledge base of a diagnostic expert system. Results from the application of the algorithm to coronary artery stenosis data are compared with the results obtained from the existing expert system.

This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation.

The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets.

The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields.

The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future.

The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.

The purpose of this paper is to establish customers’ markets and rules of dynamic customer relationship management (CRM) systems for online retailers.

This research proposes a procedure to discover customers’ markets and rules, which adopts the recency, frequency, monetary value (RFM) variables, transaction records, and socioeconomic data of the online shoppers to be the research variables. The research methods aim at the supervised apriori algorithm, C5.0 decision tree algorithm, and RFM model.

This research discovered eight RFM markets and six rules of online retailers.

The proposed framework and research results can help retailer managers to retain and expand high value markets via their dynamic CRM and POS systems.

This research uses data mining technologies to extract high value markets and rules for marketing plans. The research variables are easy to obtain via retailers’ systems. The found customer values, RFM markets, shopping association rules, and marketing decision rules can be discovered via the framework of this research.

To show the successful use of self‐organising fuzzy control in enhancing dynamic optimisation, a controller is used to direct the type of optimisation appropriate in each new dynamic problem. The system uses its experiences to determine which approach is most suitable under varying circumstances.

A knowledge extraction tool is used to gain basic information about the solution space with a simple computation. This information is compared with the fuzzy rules stored in the system. These rules hold a collection of facts on previous successes and failures, which were acquired through the performance monitor. Using this system the controller directs the algorithms, deciphering the most appropriate strategy for the current problem.

This procedure is designed for large scale dynamic optimisation problems, where a portion of the computational time is sacrificed to allow the controller to direct the best possible solution strategy. The results here are based on smaller scale systems, which illustrate the benefits of the technique.

The results highlight two significant aspects. From the comparison of the three algorithms without the use of the controller, a pattern can be seen in how the algorithms perform on different types of problems. Results show an improvement in the overall quality when the controller is employed.

This paper introduces a novel approach to the problem dynamic optimisation. It combines the control ability of self‐organising fuzzy logic with a range of optimisation techniques to obtain the best possible approach in any one situation.

Presents an inductive machine learning algorithm called CLILP2 that learns multiple covers for a concept from positive and negative examples. Although inductive learning is an error‐prone process, multiple meaning interpretation of the examples is utilized by CLILP2 to compensate for the narrowness of induction. The algorithm is tested on data sets representing three different domains. Analyses the complexity of the algorithm and compares the results with those obtained by others. Employs measures of specificity, sensitivity, and predictive accuracy which are not usually used in presenting machine learning results, and shows that they evaluate better the “correctness” of the learned concepts. The study is published in two parts: I – the CLILP2 algorithm; II – experimental results and conclusions.