Association Rule Mining Considering Local Frequent Patterns with Temporal Intervals

Recently Negative Association Rule Mining (NARM) has become a focus in the field of spatial data mining. Negative association rules are useful in data analysis to identify objects that conflict with each other or that complement each other. Much effort has been devoted for developing algorithms for efficiently discovering relation between objects in space. All the traditional association rule mining algorithms were developed to find positive associations between objects. By positive correlation we refer to associations between frequently occurring objects in space such as a city is always located near a river and so on. Recently the problem of identifying negative associations (or “dissociations”) that is absence of objects has been explored and considered relevant. This paper presents an improved design approach for mining both positive and negative association rules in spatial databases. This approach extends traditional association rules to include negative association rules using a minimum support count. Experimental results show that this approach is efficient on simple and sparse datasets when minimum support is high to some degree, and it overcomes some limitations of the previous mining methods. The proposed form will extend related applications of negative association rules to a greater extent.

Incremental sequential patterns for multivariate temporal association rules mining

Big data has several applications like page bank algorithm, recommendation system, sentimental analysis, and many more. Recommendation is the key to success for discovering and retrieval of content in this era of huge data [1]. Association Rule Mining (ARM) algorithm is mostly used for recommendation systems. User behaviour is given input to ARM in key/value pair format which can be generated by Map-Reduce function [2]. ARM attempts to find frequent item sets among large datasets and describes the association relationships among different attributes. Market-Basket analysis is the major application of ARM used to predict customer's purchasing behaviour and recommend product to customer [3]. For implementing this recommendation systems there are several programming languages and scala is one among them which is an object oriented and also supports functional programming [4]. For processing large amount of data, functional programming is faster and easier, but in some cases we need to provide security for sensitive data. Also scala runs on Java Virtual Machine (JVM) and also scala can execute Java code since scala code is converted to Java Byte code when compiled. So scala will have a better advantage to implement big data applications.

A provided data from a transactional database, has supported technical development which can automatically find product association or items saved in the database. This finding association rules between saved product in database known as mining association rules. There are so many theories and algorithm developed for conducting mining association rules. One of algorithm developed is Apriori algorithm. This method, has a main goal to find the maximum frequent itemset. Next, this frequent itemset will be generated into associative rules which are not shown before in the database, become valuable information for considering materials in the decision process. Apriori algorithm is a interpretation technique of mining association rules, will be implemented into a web based software. On the software test which use in some different data, it’s concluded that time for mining association rules depends on the presence of every item in every transaction, total of transaction, minimum support and minimum confidence. For smaller value of minimum support and minimum confidence that entered, program will generate more association rules, vice versa.

Association rule mining (ARM) is well-known issue in data mining. It is a combinatorial optimization problem purpose to extract the correlations between items in sizable data-sets. According to the literature study, bio-inspired prove their efficiency in term of time, memory and quality of generated rules. This paper investigates multi-population cooperative version of bat algorithm for association rule mining (BAT-ARM) named MPB-ARM which is based on bat inspired algorithm. The advantage of bat algorithm is the power combination between population-based algorithm and the local search, however, it more powerful in local search. The main factor to judge optimization algorithms is ensuring the interaction between global diverse exploration and local intensive exploitation. To maintain the diversity of bats, in our proposed approach, we introduce a cooperative master-slave strategy between the subpopulations. The experimental results shows that our proposal outperforms other bio-inspired algorithms already exist and cited in the literature including our previous work BAT-ARM.

DATA mining is a vital analysis domain today that focuses on data discovery in databases. It’s wherever knowledge from the database are mined in order that informative knowledge will be made and used effectively and with efficiency by humans. Prediction and description are its objectives. One amongst the aspects of information mining is that the Association Rule mining. It consists of two procedures: First, finding the frequent item set within the info employing a minimum support and constructing the association rule from the frequent item set with such confidence. It relates to the association of things whereby for each prevalence of A, there exists an occasion of B. This mining is additional applicable within the market basket analysis. Purchasers who buy certain items will benefit from that application. What is the attainable item that matches every item that they purchase? Among the most widely used association rule mining algorithms are MLPs and genetic algorithms.

The library of Faculty of Mathematics and Natural Science (FMIPA) has a collection of books and other print media, total of 2,678 books with 7237 visitors and 2148 borrowers. The available book search system was very helpful for visitors to find the required books. Especially if the system has features recommended of books. In the provision of book recommendations used one of the data mining techniques, namely association rule mining techniques or excavation of association rules. In the development of this recommendation system, KDD (Knowledge Discovery from Database) model was used. The data used was the transaction history of borrowing book with the category of "chemistry", for the last 5 (five) months, that is September 2014 - February 2015. The excavation technique of this association rule has 2 (two) main process, they are: frequent patterns and rules. To find frequent patterns, a CT-PRO algorithm was used. The minimum value of support used was 1 and 2. Once the pattern is found, the confidence value of each pattern was calculated. The minimum value of confidence used ranges from 10% to 100%. The recommendation rule was based on calculating the value of this confidence. The comparison of minimum support values indicates that the greater value of minimum support then the less borrowing pattern was generated, and vice versa. The comparison of minimum confidence value shows that the greater of minimum confidence value then the less recommended rule given.. Keywords: Library, Recommended System, Knowledge Discovery from Database (KDD), Association Rule Mining, CT-PRO Algorithm

Temporal association rule mining is a variation of association rule mining for finding relationship between items with respect to particular time periods. It is very useful in making business-related decisions, such as catalogue design, cross-marketing, cross-selling, and inventory control. Further, for effective inventory management, economic order quantity (EOQ) of items is determined by considering cross-selling effect among items. In this paper, we propose a new method for EOQ estimation. The method is based on finding the strongest relation between items by using temporal association rule mining. First, opportunity cost of frequent item-sets is determined by the temporal association rule mining, and then this opportunity cost is used to determine the EOQ for imperfect quality items. Furthermore, effect on ordering policy is determined for imperfect quality items and perfect quality items by considering cross-selling effect. A numerical example is illustrated to validate the results.

Big data has become one of the key sources for valuable information and as information becomes larger it poses some computational challenge in finding a best possible solution for mining association rules and discovering patterns in data. Meta-heuristic algorithm when applied to mining association rules aims to find best possible rules from data without being stuck in local optimal. Example of meta-heuristics algorithm includes genetic algorithm and particle swarm optimization algorithm. Finding appropriate representation of various types of patterns using rough numerical values attributes is still a challenge because most association rules cannot be applied to numerical data without discretization which may lead to information loss. Mining numeric association rules is a hard optimization problem rather than being a discretization, thus, this paper proposes a new meta-heuristic algorithm which uses wolf search algorithm (WSA) for numeric association rule mining from rough values within tolerable ranges.

Association rule mining is one of the most popular pattern discovery methods used in data mining. Frequent pattern extraction is an essential step in association rule mining. Most of the proposed algorithms for extracting frequent patterns are based on the downward closure lemma concept utilizing the support and confidence framework. In this chapter we investigate an alternative method for mining frequent patterns in a transactional database. Self-Organizing Map (SOM) is an unsupervised neural network that effectively creates spatially organized internal representations of the features and abstractions detected in the input space. It is one of the most popular clustering techniques, and it reveals existing similarities in the input space by performing a topology-preserving mapping. These promising properties indicate that such a clustering technique can be used to detect frequent patterns in a top-down manner as opposed to the traditional approach that employs a bottom-up lattice search. Issues that are frequently raised when using clustering technique for the purpose of finding association rules are: (i) the completeness of association rule set, (ii) the support level for the rules generated, and (iii) the confidence level for the rules generated. We present some case studies analyzing the relationships between the SOM approach and the traditional association rule framework, and propose a way to constrain the clustering technique so that the traditional support constraint can be approximated. Throughout our experiments, we have demonstrated how a clustering approach can be used for discovering frequent patterns.

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.

Along with the transformation of technical era, the technical progression has also raised concerns near the security of web doings. These activities remain in a way or the other are tried to be cooperated by the opponent with the aim of ahead knowledge which may be someway useful for him/her. In addition, fanatics are also operating web for gratifying their merciless goals which is currently an utmost concern for security agencies. Although there are many successful attempts have been made to restrict the existence of these illegitimate people, there still is a need for an effective assertion solution. In respect to this, data mining comes out as a solution by bringing into existence a mining concept named Terrorist Network Mining. Guerrilla network mining has proved as the most feasible solution where detection and analysis of terrorists is well performed. Still there were some improvements required to this concept which was efficiently done by uniting fuzzy with genetic procedures with the interruption discovery system (IDS) resulting into significant and efficient detection process. Hence the paper discusses about how well an intrusion detection system performs when combined with fuzzy data mining (reveal patterns whose behavior is intrusive) with genetic algorithm (leads to the achievement of efficient detection of intruders).

Discovering the relationship between behavior and personality of learner in the web-based learning environment is a key to guide learners in the learning process. This paper proposes a new concept called personality mining to find the “deep” personality through the observed data about the behavior. First, a learner model which includes personality model and behavior model is proposed. Second, we have designed and implemented an improved algorithm, which is based on Apriori algorithm widely used in market basket analysis, to identify the relationship. Third, we have discussed various issues like constructing the learner model, unifying the value domain of heterogeneous model attributes, and improving Apriori algorithm with decision domain. Experiment result indicated that this algorithm for mining association rules between behavior and personality is feasible and efficient. The algorithm has been used in a web-based learning environment developed at Xi’an Jiaotong University.

Geographic object flow is the reason behind the relationship of geographic units. There are interactions in the process of dynamic change of a geographic object flow, and its regularity, which can reflect the relationship or pattern of geographic units in a region. In this paper, an association rule mining method for the geographic object flow linkage process is studied from a geoeconomics perspective. Additionally, an association rule mining algorithm with hierarchical constraints is proposed. Data segmentation is performed according to the time series characteristics of geographic object flow data. The basic attributes for the association rule mining are determined based on the basic parameters of geographic object flows, and a database for the association rule mining is formed according to the characteristics of the hierarchical structure of the geographic object flows. Based on the obtained data, the association rule mining algorithm with hierarchical constraints obtained using a parent–child matrix is improved by adding the Apriori algorithm. With the Indo-Pacific region as an example, the trade flow association rules for 25 countries in the region from 2010 to 2021 are selected. In addition, a mathematical statistical analysis of the strongly associated mined trade flows and geoeconomic factors is conducted in terms of both a basic feature analysis of trade flow associations and a country-oriented trade flow association analysis by considering domain knowledge. The effectiveness of the method has been evaluated from various perspectives such as correlation analysis, mathematical statistics, and comparison with the findings of existing studies and proved the validity of the method.

Protein-protein interactions (PPIs) have been widely used to understand different biological processes and cellular functions associated with several diseases like cancer. Although some cancer-related protein interaction databases are available, lack of experimental data and conflicting PPI data among different available databases have slowed down the cancer research. Therefore, in this study, the authors have focused on various proteins that are directly related to different types of cancer disease. They have prepared a PPI database between cancer-associated proteins with the rest of the human proteins. They have also incorporated the annotation type and direction of each interaction. Subsequently, a biclustering-based association rule mining algorithm is applied to predict new interactions with type and direction. This study shows the prediction power of association rule mining algorithm over the traditional classifier model without choosing a negative data set. The time complexity of the biclustering-based association rule mining is also analysed and compared to traditional association rule mining. The authors are able to discover 38 new PPIs which are not present in the cancer database. The biological relevance of these newly predicted interactions is analysed by published literature. Recognition of such interactions may accelerate a way of developing new drugs to prevent different cancer-related diseases.