Weighted Association Rule Mining Research Articles

Gain ratio based fuzzy weighted association rule mining classifier for medical diagnostic interface

The health care environment still needs knowledge based discovery for handling wealth of data. Extraction of the potential causes of the diseases is the most important factor for medical data mining. Fuzzy association rule mining is well-performed better than traditional classifiers but it suffers from the exponential growth of the rules produced. In the past, we have proposed an information gain based fuzzy association rule mining algorithm for extracting both association rules and member-ship functions of medical data to reduce the rules. It used a ranking based weight value to identify the potential attribute. When we take a large number of distinct values, the computation of information gain value is not feasible. In this paper, an enhanced approach, called gain ratio based fuzzy weighted association rule mining, is thus proposed for distinct diseases and also increase the learning time of the previous one. Experimental results show that there is a marginal improvement in the attribute selection process and also improvement in the classifier accuracy. The system has been implemented in Java platform and verified by using benchmark data from the UCI machine learning repository.

A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules

In associative classification method, the rules generated from association rule mining are converted into classification rules. The concept of association rule mining can be extended in web mining environment to find associations between web pages visited together by the internet users in their browsing sessions. The weighted fuzzy association rule mining techniques are capable of finding natural associations between items by considering the significance of their presence in a transaction. The significance of an item in a transaction is usually referred as the weight of an item in the transaction and finding associations between such weighted items is called fuzzy weighted association rule mining. In this paper, we are presenting a novel web classification algorithm using the principles of fuzzy association rule mining to classify the web pages into different web categories, depending on the manner in which they appear in user sessions. The results are finally represented in the form of classification rules and these rules are compared with the result generated using famous Boolean Apriori association rule mining algorithm.

Integrated Web Recommendation Model with Improved Weighted Association Rule Mining

Integrated Web Recommendation Model with Improved Weighted Association Rule Mining

Analyzing the Coronary Heart Events Based on Weighted Association Rule Mining

Coronary heart disease (CHD) is one of the major causes of disability in adults as well as one of the main causes of death in the developed countries. Although significant progress has been made in the diagnosis and treatment of CHD, further investigation is still needed. The objective of this study was to develop the assessment of heart event-risk factors targeting in the reduction of CHD events using Weighted Association Rule Mining. The risk factors investigated were: 1) before the event: a) nonmodifiable—age, sex, and family history for premature CHD, b) modifiable—smoking before the event, history of hypertension, and history of diabetes; and 2) after the event: modifiable—smoking after the event, systolic blood pressure, diastolic blood pressure, total cholesterol, highdensity lipoprotein, low-density lipoprotein, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABGData-mining analysis was carried out using the Weighted Association Rule Mining for the afore mentioned three events using five different splitting criteria.

Rule-Based Semantic Concept Classification from Large-Scale Video Collections

The explosive growth and increasing complexity of the multimedia data have created a high demand of multimedia services and applications in various areas so that people can access and distribute the data easily. Unfortunately, traditional keyword-based information retrieval is no longer suitable. Instead, multimedia data mining and content-based multimedia information retrieval have become the key technologies in modern societies. Among many data mining techniques, association rule mining (ARM) is considered one of the most popular approaches to extract useful information from multimedia data in terms of relationships between variables. In this paper, a novel rule-based semantic concept classification framework using weighted association rule mining (WARM), capturing the significance degrees of the feature-value pairs to improve the applicability of ARM, is proposed to deal with major issues and challenges in large-scale video semantic concept classification. Unlike traditional ARM that the rules are generated by frequency count and the items existing in one rule are equally important, our proposed WARM algorithm utilizes multiple correspondence analysis (MCA) to explore the relationships among features and concepts and to signify different contributions of the features in rule generation. To the authors best knowledge, this is one of the first WARM-based classifiers in the field of multimedia concept retrieval. The experimental results on the benchmark TRECVID data demonstrate that the proposed framework is able to handle large-scale and imbalanced video data with promising classification and retrieval performance.

Order Batching in Warehouses by Minimizing Total Tardiness: A Hybrid Approach of Weighted Association Rule Mining and Genetic Algorithms

One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

Fuzzy weighted association rule based solution approaches to facility layout problem in cellular manufacturing system

This study aims to propose new solution approaches based on fuzzy weighted association rules for facility layout problem in a cellular manufacturing system. The facility layout problem under concern has two important aspects, namely; fuzzy product route and fuzzy machine weight. Number of machine types as well as the number of machines in each type is determined in order to define a fuzzy product route. A fuzzy set is then assigned for each machine type to match them with appropriate products. Alternative routes for each product and machine weights are defined by fuzzy functions. Market demand is also used to assign weights to the machines. Three solution approaches, namely fuzzy weighted association rule mining(FWARM), Gyenesei’s weighted quantitative association rules (WQAR) and normalised weighted association rule mining(NWARM) based approaches, are proposed in this study. To the authors’ best knowledge, this is the first attempt which considers fuzzy weighted association rule-based data mining approaches to the facility layout problem. An illustrative example is presented to demonstrate the usefulness of the proposed approaches.

A Hybrid Web Recommendation System Based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommendation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on association rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the frequency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages that are not yet visited by users is not included in the recommendation set. To overcome this problem, we have used the web usage log in the adaptive association rule based web mining where the association rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.

Weighted Association Rules Mining Algorithm Research

Aiming at the problem that most of weighted association rules mining algorithms have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, weighted boolean association rules mining algorithm and weighted fuzzy association rules mining algorithm are presented, which use pruning strategy of Apriori algorithm so that improve the efficiency of frequent itemsets generated. Experimental results show that both algorithms have good performance.

Weighted association rule mining via a graph based connectivity model

Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Classical association rule mining approaches make the implicit assumption that an item’s importance is determined by its support. In contrast, Weighted Association Rule Mining (WARM) attempts to provide a notion of importance, or weight to individual items that are not based solely on item support. Previous approaches to Weighted Association Rule Mining assign item weights in a subjective manner, based on a user’s specialized knowledge of the underlying domain that is involved. Such approaches are infeasible when millions of items are present in a dataset, or when domain knowledge is unavailable. Furthermore, even when such domain information is available, a weight assignment based on subjective information constrains the knowledge discovered to fit with the weights assigned, thus inhibiting the discovery of new trends in the data. In this research we automate the process of weight assignment by formulating a linear model that captures relationships between items. This approach extends prior research based on the Valency model. We extend the Valency model by expanding the field of interaction beyond immediate neighborhoods and show that this leads to significant improvements in performance on a number of different metrics that we use.

Fuzzy Weighted Associative Classifier : A Predictive Technique for Health Care Data Mining

In this paper we extend the problem of classification using Fuzzy Association Rule Mining and propose the concept of Fuzzy Weighted Associative Classifier (FWAC). Classification based on Association rules is considered to be effective and advantageous in many cases. Associative classifiers are especially fit to applications where the model may assist the domain experts in their decisions. Weighted Associative Classifiers that takes advantage of weighted Association Rule Mining is already being proposed. However, there is a so-called sharp boundary problem in association rules mining with quantitative attribute domains. This paper proposes a new Fuzzy Weighted Associative Classifier (FWAC) that generates classification rules using Fuzzy Weighted Support and Confidence framework. The naive approach can be used to generating strong rules instead of weak irrelevant rules. where fuzzy logic is used in partitioning the domains. The problem of Invalidation of Downward Closure property is solved and the concept of Fuzzy Weighted Support and Fuzzy Weighted Confidence frame work for Boolean and quantitative item with weighted setting is generalized. We propose a theoretical model to introduce new associative classifier that takes advantage of Fuzzy Weighted Association rule mining.

Temporal Weighted Association Rule Mining for Classification

Temporal Weighted Association Rule Mining for Classification

Compact Weighted Class Association Rule Mining Using Information Gain

Weighted association rule mining reflects semantic significance of item by considering its weight. Classification constructs the classifier and predicts the new data instance. This paper proposes compact weighted class association rule mining method, which applies weighted association rule mining in the classification and constructs an efficient weighted associative classifier. This proposed associative classification algorithm chooses one non class informative attribute from dataset and all the weighted class association rules are generated based on that attribute. The weight of the item is considered as one of the parameter in generating the weighted class association rules. This proposed algorithm calculates the weight using the HITS model. Experimental results show that the proposed system generates less number of high quality rules which improves the classification accuracy.

Compact Weighted Class Association Rule Mining Using Information Gain

Weighted association rule mining reflects semantic significance of item by considering its weight. Classification constructs the classifier and predicts the new data instance. This paper proposes compact weighted class association rule mining method, which applies weighted association rule mining in the classification and constructs an efficient weighted associative classifier. This proposed associative classification algorithm chooses one non class informative attribute from dataset and all the weighted class association rules are generated based on that attribute. The weight of the item is considered as one of the parameter in generating the weighted class association rules. This proposed algorithm calculates the weight using the HITS model. Experimental results show that the proposed system generates less number of high quality rules which improves the classification accuracy.

Automatic Item Weight Generation for Pattern Mining and its Application

Association rule mining discovers relationships among items in a transactional database. Most approaches assume that all items within a dataset have a uniform distribution with respect to support. However, this is not always the case, and weighted association rule mining (WARM) was introduced to provide importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. In certain cases, it is difficult to provide weights to all items within a dataset. In this paper, the authors propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. The authors experiment shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared with a miner that does not employ a weighting scheme. The authors applied the model in a real world application to mine text from a given collection of documents. The use of item weighting enabled the authors to attach more importance to terms that are distinctive. The results demonstrate that keyword discrimination via item weighting leads to informative rules.

Weighted approximate sequential pattern mining within tolerance factors

In data mining area, weight based sequential pattern mining has been suggested to find important sequential patterns by considering the weights of sequential patterns. More extensions with weight constraints have been proposed such as mining weighted association rules, weighted sequential patterns, weighted closed patterns, frequent patterns with dynamic weights, weighted graphs, weighted sub-trees or sub structures, and so on. In previous approach of weighted frequent sequential pattern mining, weighted supports of sequential patterns are exactly matched to prune weighted infrequent sequential patterns. However, in the noisy environment, the small change in weights or supports of items affects the result sets seriously. This may make the weighted sequential patterns less useful in the noisy environment. In this paper, we propose the robust concept of mining weighted approximate sequential patterns. Based on the framework of weight based sequential pattern mining, an approximate factor is defined to relax the requirement for exact equality between weighted supports of sequential patterns and a minimum threshold. After then, we address the concept of mining weighted approximate sequential frequent patterns to find important sequential patterns with/without the noisy data. We analyze the characteristics of weighted approximate sequential patterns and run extensive performance tests.

A Fuzzy Approach to Weighted Temporal Mining Using Automatic Weight Assignment with Reduced Complexities

Databases and data warehouses have become a vital part of many organizations. So useful information and helpful knowledge have to be mined from transactions. In real life, media information has time attributes either implicitly or explicitly called as temporal data. The temporal database consists of items which are prioritized by assigning weights. These weights are assigned automatically using the scheduling concept aging algorithm in such a way that starvation is avoided. This paper focuses on an encoding method for the temporal database that reduces the memory utilization during processing. The fuzzy approach is applied by assigning the triangular membership functions to the weighted items which gives better results than quantitative values. Weighted association rule mining is performed using the weighted support and confidence measures which are applied to the AprioriTid algorithm as a new approach to the already existing algorithm. This makes the modified algorithm compatible to mine weighted association rules. This approach gives better results than that which treats items uniformly, leading to weighted association rule mining on an encoded temporal database with reduced time and computational complexity. The experimental results are drawn from the complaints database of the telecommunication system. This database technology can be used to enhance the complaint addressing system through the web which is an upgraded technique for the semantic web.

Approximate weighted frequent pattern mining with/without noisy environments

In data mining area, weighted frequent pattern mining has been suggested to find important frequent patterns by considering the weights of patterns. More extensions with weight constraints have been proposed such as mining weighted association rules, weighted sequential patterns, weighted closed patterns, frequent patterns with dynamic weights, weighted graphs, and weighted sub-trees or sub structures. In previous approaches of weighted frequent pattern mining, weighted supports of patterns were exactly matched to prune weighted infrequent patterns. However, in the noisy environment, the small change in weights or supports of items affects the result sets seriously. This may make the weighted frequent patterns less useful in the noisy environment. In this paper, we propose the robust concept of mining approximate weighted frequent patterns. Based on the framework of weight based pattern mining, an approximate factor is defined to relax the requirement for exact equality between weighted supports of patterns and a minimum threshold. After that, we address the concept of mining approximate weighted frequent patterns to find important patterns with/without the noisy data. We analyze characteristics of approximate weighted frequent patterns and run extensive performance tests.

Weighted Association Rule Mining for Video Semantic Detection

Semantic knowledge detection of multimedia content has become a very popular research topic in recent years. The association rule mining (ARM) technique has been shown to be an efficient and accurate approach for content-based multimedia retrieval and semantic concept detection in many applications. To further improve the performance of traditional association rule mining technique, a video semantic concept detection framework whose classifier is built upon a new weighted association rule mining (WARM) algorithm is proposed in this article. Our proposed WARM algorithm is able to capture the different significance degrees of the items (feature-value pairs) in generating the association rules for video semantic concept detection. Our proposed WARM-based framework first applies multiple correspondence analysis (MCA) to project the features and classes into a new principle component space and discover the correlation between feature-value pairs and classes. Next, it considers both correlation and percentage information as the measurement to weight the feature-value pairs and to generate the association rules. Finally, it performs classification by using these weighted association rules. To evaluate our WARM-based framework, we compare its performance of video semantic concept detection with several well-known classifiers using the benchmark data available from the 2007 and 2008 TRECVID projects. The results demonstrate that our WARM-based framework achieves promising performance and performs significantly better than those classifiers in the comparison.

Effective page recommendation algorithms based on distributed learning automata and weighted association rules

Different efforts have been done to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users’ interactions. In this paper, we propose three algorithms to solve the web page recommendation problem. In our first algorithm, we use distributed learning automata to learn the behavior of previous users’ and recommend pages to the current user based on learned patterns. By introducing a novel weighted association rule mining algorithm, we present our second algorithm for recommendation purpose. Also, a novel method is proposed to pure the current session window. One of the challenging problems in recommendation systems is dealing with unvisited or newly added pages. By considering this problem and improving the efficiency of first two algorithms we present a hybrid algorithm based on distributed learning automata and proposed weighted association rule mining algorithm. In the hybrid algorithm we employ the HITS algorithm to extend the recommendation set. Our experiments on real data set show that the hybrid algorithm performs better than the other algorithms we compared to and, at the same time, it is less complex than other proposed algorithms with respect to memory usage and computational cost too.