Class Association Rules Research Articles

An improved algorithm for mining class association rules using the difference of Obidsets

Class association rules play an important role in decision support systems and have thus been extensively studied. Recently, an efficient algorithm for mining class association rules, named CAR-Miner, has been proposed. It, however, consumes a lot of memory for storing the Obidsets (sets of object identifiers that contain itemsets) of itemsets and requires a lot of time to compute the intersection between two Obidsets, especially in the large datasets. This paper proposes an improved algorithm for mining class association rules that uses the difference between two Obidsets (d2O) to save memory usage and run time. Firstly, the d2O concept is developed. A strategy for reducing the storage space and computation time of d2O is then derived. Experimental results show that the proposed algorithm is more efficient than CAR-Miner in terms of run time and memory usage.

Fast Vertical Mining Using Boolean Algebra

The vertical association rules mining algorithm is an efficient mining method, which makes use of support sets of frequent itemsets to calculate the support of candidate itemsets. It overcomes the disadvantage of scanning database many times like Apriori algorithm. In vertical mining, frequent itemsets can be represented as a set of bit vectors in memory, which enables for fast computation. The sizes of bit vectors for itemsets are the main space expense of the algorithm that restricts its expansibility. Therefore, in this paper, a proposed algorithm that compresses the bit vectors of frequent itemsets will be presented. The new bit vector schema presented here depends on Boolean algebra rules to compute the intersection of two compressed bit vectors without making any costly decompression operation. The experimental results show that the proposed algorithm, Vertical Boolean Mining (VBM) algorithm is better than both Apriori algorithm and the classical vertical association rule mining algorithm in the mining time and the memory usage.

IWFPM: Interested Weighted Frequent Pattern Mining with Multiple Supports

Association rules mining has been under great attention and considered as one of momentous area in data mining. Classical association rules mining approaches make implicit assumption that items' importance is the same and set a single support for all items. This paper presents an efficient approach for mining users' interest weighted frequent patterns from a transactional database. Our paradigm is to assign appropriate minimum support (minsup) and weight for each item, which reduces the number of unnecessary patterns. Furthermore, we also extend the support-confidence framework and define an interest measure to the mining algorithm for excavating users' interested patterns effectively. In the end, experiments on both synthetic and real world datasets show that the proposed algorithm can generate more interested patterns.

Combining hybrid rule ordering strategies based on netconf and a novel satisfaction mechanism for CAR-based classifiers

In Associative Classification, building a classifier based on Class Association Rules (CARs) consists in finding an ordered CAR list by applying a rule ordering strategy, and selecting a satisfaction mechanism to determine the class of unseen transactions. In this paper, we introduce four novel hybrid rule ordering strategies; the first three combine the Netconf measure with different Support-Confidence based rule ordering strategies. The fourth strategy combines the Netconf measure with a rule ordering strategy based on the CAR's size. Additionally, we combine the proposed strategies with a novel Dynamic K satisfaction mechanism. Experiments over several datasets show that the proposed rule ordering strategies jointly with the Dynamic K satisfaction mechanism allow improving the performance of CAR-based classifiers.

Updating mined class association rules for record insertion

Mining class association rules is an interesting problem in classification and prediction. Some recent studies have shown that using classifiers based on class association rules resulted in higher accuracy than those obtained by using other classification algorithms such as C4.5 and ILA. Although many algorithms have been proposed for mining class association rules, they were used for batch processing. However, real-world datasets regularly change; thus, updating a set of rules is challenging. This paper proposes an incremental method for mining class association rules when records are inserted into the dataset. Firstly, a modified equivalence class rules tree (MECR-tree) is created from the original dataset. When records are inserted, nodes on the tree are updated by changing their information including Obidset (a set of object identifiers containing the node's itemset), count, and pos. Secondly, the concept of pre-large itemsets is applied to avoid re-scanning the original dataset. Finally, a theorem is proposed to quickly prune nodes that cannot generate rules in the tree update process. Experimental results show that the proposed method is more effective than mining entire original and inserted datasets.

RST Approach for Efficient CARs Mining

In data mining, an association rule is a pattern that states the occurrence of two items (premises and consequences) together with certain probability. A class association rule set (CARs) is a subset of association rules with classes specified as their consequences. This paper focuses on class association rules mining based on the approach of Rough Set Theory (RST). In addition, this paper presents an algorithm for finest class rule set mining inspired from Apriori algorithm, where the support and confidence are computed based on the elementary set of lower approximation inspired from RST. The proposed approach has been shown very effective, where the rough set approach for class association discovery is much simpler than the classic association method.

An evolutionary algorithm for the discovery of rare class association rules in learning management systems

Association rule mining, an important data mining technique, has been widely focused on the extraction of frequent patterns. Nevertheless, in some application domains it is interesting to discover patterns that do not frequently occur, even when they are strongly related. More specifically, this type of relation can be very appropriate in e-learning domains due to its intrinsic imbalanced nature. In these domains, the aim is to discover a small but interesting and useful set of rules that could barely be extracted by traditional algorithms founded in exhaustive search-based techniques. In this paper, we propose an evolutionary algorithm for mining rare class association rules when gathering student usage data from a Moodle system. We analyse how the use of different parameters of the algorithm determine the rule characteristics, and provides some illustrative examples of them to show their interpretability and usefulness in e-learning environments. We also compare our approach to other existing algorithms for mining both rare and frequent association rules. Finally, an analysis of the rules mined is presented, which allows information about students' unusual behaviour regarding the achievement of bad or good marks to be discovered.

Dynamic System to Discover a Pattern

It is indeed an art to match maximum number of preferences by utilizing limited number of resources. During the current academic year 75% of the admissions to Engineering Colleges have gone down, as only 30% to 40% of intake has been filled. Without reaching the breakeven point, the management of the institution becomes a complicated issue. In this situation providing quality education to the students is the question mark. The main aim of this paper is to discover a pattern to identify the choice of preferences of the candidates to seek admissions in any academic institutions. The candidate finds admission in an institution only when his/her own preference matches exactly, otherwise the candidate continues to go by the next alternate in the list of preference. If the institution analyzes the preferences of the candidates and tries to satisfy them, surely the institution can reach even above their intake. Generally satisfaction of individual candidate is practically not possible. Hence the institution should try to satisfy maximum number of candidates by utilizing our existing infrastructure and viable number of preferences. Here the viability is the main constraint. For the purpose of matching optimum number of candidates to suit our existing system, we have designed our algorithmic approach. Here our new system is used to extract frequent item sets from various preferences. By thresholds, it can fix the preferences either decrease or increase the level of frequent. The new algorithm is based on association rule classification which is one of data mining techniques. Data mining is the process of extracting knowledge hidden from large volumes of raw data

A novel associative classification model based on a fuzzy frequent pattern mining algorithm

Associative classification models are based on two different data mining paradigms, namely pattern classification and association rule mining. These models are very popular for building highly accurate classifiers and have been employed in a number of real world applications.During the last years, several studies and different algorithms have been proposed to integrate associative classification models with the fuzzy set theory, leading to the so-called fuzzy associative classifiers.In this paper, we propose a novel efficient fuzzy associative classification approach based on a fuzzy frequent pattern mining algorithm. Fuzzy items are generated by discretizing the input variables and defining strong fuzzy partitions on the intervals resulting from these discretizations. Then, fuzzy associative classification rules are mined by employing a fuzzy extension of the FP-Growth algorithm, one of the most efficient frequent pattern mining algorithms. Finally, a set of highly accurate classification rules is generated after a pruning stage.We tested our approach on seventeen real-world datasets and compared the achieved results with the ones obtained by using both a non-fuzzy associative classifier, namely CMAR, and two recent state-of-the-art classifiers, namely FARC-HD and D-MOFARC, based on fuzzy association rules. Using non-parametric statistical tests, we show that our approach outperforms CMAR and achieves accuracies similar to FARC-HD and D-MOFARC.

Mining Class Association Rules in Distributed Datasets

Mining Class Association Rules in Distributed Datasets

CCAR: An efficient method for mining class association rules with itemset constraints

Class association rules (CARs) are basically used to build a classification model for prediction; they can also be used to describe correlations between itemsets and class labels. The latter is very popular in mining medical data. For example, epidemiologists often consider rules which indicate the relations between risk factors (itemsets) and HIV test results (class labels). However, in the real world, end users are often interested in a subset of class association rules. Particularly, they may consider only rules which contain at least one itemset from a user-defined set of itemsets in the rule antecedent. For example, when classifying which populations are at high risk for HIV infection, epidemiologists often concentrate on rules that include demographic information such as sex, age, and marital status in rule antecedents. Two naive strategies are to solve this problem by applying the itemset constraints into the pre-processing or post-processing step. However, such approaches are time-intensive. This paper thus proposes an efficient method for integrating the constraints into the class association rule mining process. The experimental results show that the proposed algorithm outperforms two basic approaches in the mining time and the memory consumption. The practical benefits of our method are demonstrated by a real-life application in the HIV/AIDS domain.

Hybridization of ABC and PSO for Optimal Rule Extraction from Knowledge Discovery Database

Knowledge discovery in database (KDD) has provided a large interest in statistics, machine learning, and artificial intelligence (AI). It is a challenging task for mining the comprehensive and informative knowledge in such complex data by using the existing methods. The challenges come from many aspects, for instance, the traditional methods usually discover homogeneous features from a single source of data while it is not effective to mine for patterns combining components from multiple data sources. It is often very costly and sometimes impossible to join multiple data sources into a single data set for pattern mining. In order to extract knowledge from different datasets, we will propose a hybrid mining technique. The knowledge extraction can be done by association rule mining with the combination of Artificial Bee Colony optimization algorithm (ABC) and Particle swarm optimization (PSO). The main aim of this hybridization is to extract the optimal rules from the association rules for further classification. The accuracy will be checked in terms of optimal rule obtained from the hybridization. The best position for moving the particle will be updated by using ABC algorithm.

Tendency Mining in Dynamic Association Rules Based on SVM Classifier

A method of tendency mining in dynamic association rule based on compatibility feature vector SVM classifier is proposed. Firstly, the class association rule set named CARs is mined by using the method of tendency mining in dynamic association rules. Secondly, the algorithm of SVM is used to construct the classifier based on compatibility feature vector to classify the obtained CARs taking advantage when dealing with high complex data. It uses a method based on judging rules’ weight to construct the model. At last, the method is compared with the traditional methods with respect to the mining accuracy. The method can solve the problem of high time complexity and have a higher accuracy than the traditional methods which is helpful to make mining dynamic association rules more accurate and effective. By analyzing the final results, it is proved that the method has lower complexity and higher classification accuracy.

Research and Implementation of Text Classification Algorithm

The development of Internet and digital library has triggered a lot of text categorization methods. How to find desired information accurately and timely is becoming more and more important and automatic text categorization can help us achieve this goal. In general, text classifier is implemented by using some traditional classification methods such as Naive-Bayes (NB). ARC-BC (Associative Rule-based Classifier by Category) can be used for text categorization by dividing text documents into subsets in which all documents belong to the same category and generate associative classification rules for each subset. This classifier differs from previous methods in that it consists of discovered association rules between words and categories extracted from the training set. In order to train and test this classifier, we constructed training data and testing data respectively by selecting documents from Yahoo. The experimental result shows that the performance of ARC-BC based text categorization is very pretty efficient and effective and it is comparable to Naïve Bayesian algorithm based text categorization.

Optimum Session Interval based on Particle Swarm Optimization for Generating Personalized Ontology

A semantic web usage mining method is suggested to identify the association between consumer emotions and buying behaviors by utilizing the web log data. Fuzzy logic is used to signify the temporal conception and resource attributes for the requested URLS of web access activities. From this, a Personal Web usage Ontology is created which facilitates semantic web applications. But the limitation is less efficient in terms of accuracy and user satisfaction. Thus an innovative technique which is called Optimum Session Interval based Particle Swarm Optimization (OSIPSO) is introduced. This technique is used to find the optimum session interval. The Particle swarm optimization has no overlapping and mutation computation and it is proficient in global search. Additionally, an associative classification is used to enhance the accuracy. Associative classification is a combination of associative rule mining and classification rule mining. An experimental result shows that the proposed work has a high accuracy and high efficient.

Investigating association rules for sentiment classification of Web reviews

Sentiment Classification of web reviews or comments is an important and challenging task in Web Mining and Data Mining due to the increasing social media and e-commerce industry. This paper present...

Study the Personal Push Service of University Library Based on Big Data Mining

It expounds the big data and the relevant theoretical knowledge of big data mining, In view of the lack of effective analysis of the data resource access in delivery service of university library, this paper designs the personalized recommendation system service model of university library, with clustering analysis and association rules theory as the foundation of technology. And it introduces in detail how to cluster according to the user's attribute characteristics and how to introduce minimum support to opti-mize on the basis of the classical association rules algorithm. Experiments show that the improved algorithm can improves the utilization of library resources.

Study on the Method of Road Transport Management Information Data Mining based on Pruning Eclat Algorithm and MapReduce

Road transport management information is a class of massive and correlation data in ITS (intelligent transportation systems), and its association rules data mining has important practical significance. In order to cover the shortage of the classical association rules optimized algorithm Eclat, this paper proposed and demonstrated that candidate sets which have the project as a prefix or suffix can be pruning calculated for both the properties. Then it proposed optimized method of frequent sets calculation-a method of parallel NEclat combining with cloud programming model. This method can solve the problem that Eclat algorithm cannot be calculated by pruning, and achieve a parallel compute. The practical application showed that, this method can reduce time waste by more than 40%, and it is suitable for the data mining of transport management information association rules.

The Discussion on the Improved Association Rules Algorithm in Data Mining

The data mining can help to extract the information and knowledge with potential application value from a huge amount of data. In order to do the date mining scientifically and efficiently, this article provides an improved algorithm based on the classical association rules Aprior algorithm, taking advantage of which, we can engage in the association rules mining of the database to obtain the useful information. The improved algorithm avoids pattern matching and reduces the number of times of visiting the database, thus improves the speed of date mining to some extend.

Principal Association Mining: An efficient classification approach

Classification is one of the key tasks in business intelligence, decision science, and machine learning. Associative classification has aroused significant research interest in recent years due to its superior accuracy. Traditional association rule mining algorithms often yield many redundant and sometimes conflicting class association rules. This paper presents a new, efficient associative classification approach. This new approach produces a compact classifier with a small number of association rules, yet with good classification performance. This approach is based on a novel rule quality metric, named as Principality, which measures an association rule’s classification accuracy and coverage for a specific class. Heuristic methods utilizing the Principality metric are applied to rule pruning and associative classifier construction to produce a compact classifier. This Principal Association Mining (PAM) approach is confirmed to be effective at improving classification accuracy as well as decreasing classifier size by experiments conducted on 17 datasets.