Mining Negative Association Rules Research Articles

An Optimized Association Rules Mining Framework for Chinese Social Insurance Fund Data Auditing

Association rules mining with the Chinese social insurance fund dataset can effectively discover different kinds of errors, irregularities, and illegal acts by providing auditors with relationships among the items and therefore improve auditing quality and efficiency. However, traditional positive and negative association rules (PNARs) mining algorithms inevitably produce too many meaningless or contradictory rules when these two types of rules are mined simultaneously, which brings a huge challenge to auditors retrieving decision information. Aimed to reduce the quantity of low-reliability PNARs without missing interesting rules, this paper first proposes an improved PNARs mining algorithm with minimum correlation and triple confidence threshold to control the mined rules number by narrowing the range of confidence settings. Then, a novel pruning algorithm based on the inclusion relation of the rule’s antecedent and consequent is given to remove those redundant rules. After that, the proposed optimized PNARs mining approach is applied to the Chinese social insurance fund dataset starting with audit features influence factors mining using the Hash table. The experimental results with different datasets show that the proposed framework not only can ensure effective and interesting rules extraction but also has better performance than traditional approaches in both accuracy and efficiency, reducing the number of redundant PNARs by over 70.1% with experimental datasets and average 78.5% with auditing data mining, respectively.

Modified Genetic Algorithm and Association Rule Mining for the Retail Sector

This paper concentrates on the optimization of elementary association rule mining. The basic approach of association rule mining generates the positive association rule but focusing on both positive and negative association rule mining to find out efficient results is lacking. Thus our aim is to provide an approach to optimize all positive and negative association rules with the help of a modified genetic algorithm. A genetic algorithm is an optimization technique that provides the best possible solutions that are stronger than the other solutions. The present approach focuses on the importance of population through mean fitness value for further genetic algorithm operation. This paper also shows a comparison between normal Apriori, the Genetic Algorithm, and our proposed algorithm. Where in as a result the proposed approach worked better than others. We believe that the proposed methodology would increase the efficiency of the Decision support system of retail stores.

Classification and variable selection using the mining of positive and negative association rules

Association rules (ARs) have been applied to classification and variable selection. However, currently, only positive ARs are used for variable selection, while only special forms of positive and negative association rules (PNARs) are used for classification.The purpose of this work was to investigate variable selection and classification methods by mining another, more general form of PNARs, one that is more suitable for binary classification and variable selection problems. The algorithm for mining such PNARs exploits the downward closure property of negative itemsets. It is built based solely on items in a transactional database and on equivalence classes under the support–confidence framework. The algorithm combines the process of mining frequent itemsets and rule generation and is both sound and complete.Experimental results on 10 binary datasets of the variable selection and classification methods using the PNARs mined by the proposed algorithm show that these methods are superior to variable selection methods that use the mutual information measure and the chi-squared test and 10 popular classification algorithms, respectively.

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative than their positive counterparts. It is well-known that during negative association rules mining, a large number of uninteresting rules are formed, making this a difficult problem to tackle. In this research, we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users’ privacy. The applied approach dynamically determines the transactions to be interrupted for information hiding, as opposed to predefining them. This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining, one that is based on the Tabu-genetic optimization paradigm. Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets. Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions, as measured by the indicator of hiding failure.

Determining context of association rules by using machine learning

ABSTRACT Association rule mining is typically used to uncover the enthralling interdependencies between the set of variables and reveals the hidden pattern within the dataset. The associations are identified based on co-occurring variables with high frequencies. These associations can be positive (A→B) or negative (A→⌐B). The number of these association rules in larger databases are considerably higher which restricted the extraction of valuable insights from the dataset. Some rule pruning strategies are used to reduce the number of rules that can sometimes miss an important, or include an unimportant rule into the final rule set because of not considering the context of the rule. Context-based positive and negative association rule mining (CBPNARM) for the first time included context variable in the algorithms of association rule mining for selection/ de-selection of such rules. In CBPNARM, the selection of context variable and its range of values are done by the user/expert of the system which demands unwanted user interaction and may add some bias to the results. This paper proposes a method to automate the selection of context variable and selection of its value range. The context variable is chosen by using the diversity index and chi-square test, and the range of values for the context variable is set by using box plot analysis. The proposed method on top of it added conditional-probability increment ratio (CPIR) for further pruning uninteresting rules. Experiments show the system can select the context variable automatically and set the right range for the selected context variable. The performance of the proposed method is compared with CBPNARM and other state of the art methods.

WITHDRAWN: Mining of negative association rules using correlate frequent pattern tree

Associative rule mining is the discovery of rules from the item set constraint that contain a set of specific items in antecedent rule and a class label. Frequent Pattern (FP-Tree) is associative rule mining method that doesn’t provide large set of candidate sets. Recently, the researchers are focusing on finding the infrequent item set leads to Negative Association Rule (NAR) mining. In this research, (FP-Tree) is added with the linkage table called Correlated Frequent Pattern Tree (CFPT) that allows the method to analyze the patterns with frequent items. The Coverage Co-efficient Measure (CCM) is applied in the FP-Tree with linkage table to identify the correlated item sets. In the mining process, CCM helps to remove irrelevant item set from the transaction. The developed IFP-Tree reduces the memory usage and the execution time in mining the frequent pattern. The four datasets have used to experiment the performance of proposed CFPT technique. The execution time of proposed CFPT method is the 0.043 s in frequent pattern mining and 0.075 s in infrequent pattern mining.

A novel interestingness measure based on fusion model for association rules mining

Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules.

Positive and negative association rule mining in Hadoop\u2019s MapReduce environment

In this paper, we present a Hadoop implementation of the Apriori algorithm. Using Hadoop’s distributed and parallel MapReduce environment, we present an architecture to mine positive as well as negative association rules in big data using frequent itemset mining and the Apriori algorithm. We also analyze and present the results of a few optimization parameters in Hadoop’s MapReduce environment as it relates to this algorithm. The results are presented based on the number of rules generated as well as the run-time efficiency. We find that, a higher amount of parallelization, which means larger block sizes, will increase the run-time efficiency of the Hadoop implementation of the Apriori algorithm.

MEMNAR: finding mutually exclusive mutation sets through negative association rule mining

MEMNAR: finding mutually exclusive mutation sets through negative association rule mining

NP-FARM: Negative and Positive Fuzzy Association Rule Mining in Transaction Dataset

Association Rule Mining (ARM) is a well recognized and interesting area of research in the field of Data Mining. In ARM, significant amount of research work has been reported. On the contrary, very less work has been reported for Negative Association Rule Mining (NARM). ARM concentrates only on positive rules while NARM explores negative rules. Also, most researchers have worked with discrete transaction dataset. So, in this paper, we propose a technique called Negative and Positive Fuzzy Association Rule Mining (NP-FARM), which mines both negative and positive association rules from a fuzzy transaction dataset. NP-FARM algorithm has been implemented and the experimental results determine the optimal minimum support threshold and optimal minimum confidence threshold for the given dataset. Also, the experimental results demonstrate that, as the size of the dataset increases, a negligible change in execution time is witnessed to mine the growing dataset. Keywords: Association Rule Mining. Fuzzy Transactions, Positive and Negative Item Sets

Study on Multidimensional Negative Association Rules

With the deepening of the negative association rules mining technology research, many key problems have been solved, but the solution of these problems are all on a single predicate in the transaction database. However, the data in the database often involves multiple predicates. This paper focuses on solving multi-dimensional support and confidence, negative association rules mining algorithm design problems. The experiment proves that the algorithm is correct and efficiency.

Efficient negative association rule mining based on chance thresholds

Typically association rule mining only considers positive frequent itemsets in rule generation, where rules involving only the presence of items are generated. In this paper we consider the complementary problem of negative association rule mining, which generates rules describing the absence of itemsets from transactions. We describe a new approach called MINR Mining Interesting Negative Rules to efficiently find all interesting negative association rules. In our approach, we only consider the presence or absence of itemsets that are strongly associated. Our approach does not require a user defined support threshold, and is based on pruning coincidental itemsets. For every individual itemset we calculate two custom thresholds based on their support: the positive and negative chance thresholds. Itemsets whose support falls above their positive chance threshold are considered positively associated whereas itemsets whose support falls below its negative chance threshold are considered negatively associated. We compared our implementation against Pearson Φ correlation and we note that MINR was able to generate sets of rules which are more interesting.

負の相関ルールを抽出する高速トップダウン型アルゴリズム

負の相関ルールを抽出する高速トップダウン型アルゴリズム

Enhanced Data Processing Using Positive Negative Association Mining on AJAX Data

Knowledge discovery is the process of analyzing data from different perspectives and summarizing it into useful information. (1) Association rule mining is a data mining process used widely in traditional databases to find the positive association rules. Association rules are created by analyzing data for frequent patterns and using the criteria support and confidence to identify the most important relationships. However, there are some other challenging rule mining topics like negative association rule mining. Organizations want to concentrate on their own business and outsource the rest of their work. This approach is named ''Software as a Service Concept'' and provides lots of benefits to data owner, but, at the same time, brings out some security problems. In this research, a rule mining approach has been proposed that provides efficient and secure solution to positive and negative association rule computation on AJAX data streams in software as a service (SaaS) concept. It is important to note that SaaS typically encapsulates enterprise as opposed to consumer- oriented web-hosted software. Software as a service (SaaS) is a way of delivering applications over the Internet—as a service. Instead of installing and maintaining software, you simply access it via the Internet, freeing yourself from complex software and hardware management. SaaS applications are sometimes called Web-based software, on-demand software, or hosted software. By using (3) AJAX, we get the search result in the form of semantic. Index Terms: Association rules mining, Ajax data stream, horizontal tree approach, Apriori algorithm

Personality Differences and Hotel Web Design Study Using Targeted Positive and Negative Association Rule Mining

As people have unique tastes, the way to satisfy a small group of targeted customers or to be generic to meet most people's preference has been a traditional question to many fashion designers and website developers. This study examined the relationship between individuals’ personality differences and their web design preferences. Each individual's personality is represented by a combination of five traits, and 15 website design-related features are considered to test the users’ preference. We introduced a data mining technique called targeted positive and negative association rule mining to analyze a dataset containing the survey results collected from undergraduate students. The results of this study not only suggest the importance of providing specific designs to attract individual customers, but also provide valuable input on the Big Five personality traits in their entirety.

Mining Positive and Negative Association Rule from Frequent and Infrequent Pattern based on IMLMS_GA

Association rule mining is one of the most significant tasks in data mining. The essential concept of association rule is to mine the positive patterns from transaction database. But mining the negative patterns has also received the interest of publishers in this region. This paper shows an efficient algorithm (IMLMS-GA) for mining both positive and negative association rules in transaction databases. The goal of this study is to build up a new model for mining negative and positive (PR & NR) association rules out of transaction data sets. The proposed model is based on two models, the MLMS model and the Interesting Multiple Level Minimum Supports (IMLMS) model. This paper proposes a new approach (IMLMS-GA) for mining both negative and positive association rules. The interesting frequent patterns and infrequent patterns mined by the IMLMS-GA algorithm. This algorithm is accomplished in two phase: a. First phase find all frequent patterns & infrequent patterns b. Second phase efficiently generate positive and negative association rule by using useful frequent pattern set. The experimental results prove that the IMLMS-GA can remove the scale of uninteresting association rules and generates better results than the previous positive and negative association rule mining algorithm.

A Study of Negative Association Rules Mining Algorithm Based on Multi-Database

the mutual exclusion relationships among data items are reflected by negative association rules, whitch is very important on the decision-making analysis. In the last several years, negative association rules are frequently researched, while the study object of it is single mining of database now. With the development of database technology, multi-database mining is more and more important. On the basis of analyzing the related technology, research status and shortage of present negative association rules mining , the selecting rules, weighted synthesis and algorithm are discussed on multi-datobase.

Effective Identification of Negative Regulation Patterns of Protein Kinases

Recent studies point to the fact that protein kinases play an important role in the regulation of cellular pathways and show great potential in disease treatment. Thus, it is critical to discover characterized regulatory patterns of protein kinases in signaling pathway. There have been considerable efforts to explore the activities of protein kinases. However, the study of negative regulation patterns has been largely overlooked and undeveloped. This paper aims to identify inhibitory regulatory correlations of protein kinase according to negative association rule mining. Especially, mutual information is applied to sort out the items with strong dependency and the minimum support threshold is computed by support constraints to control rule generation. The obtained rules not only reveal the relationships between subunits of protein kinases and between subunits and stimuli, but also provide novel pharmacological insight into drug design for diseases.

INTEGRATING ACTION-BASED DEFECT PREDICTION TO PROVIDE RECOMMENDATIONS FOR DEFECT ACTION CORRECTION

Reducing software defects is an essential activity for Software Process Improvement. The Action-Based Defect Prediction (ABDP) approach fragments the software process into actions, and builds software defect prediction models using data collected from the execution of actions and reported defects. Though the ABDP approach can be applied to predict possible defects in subsequent actions, the efficiency of corrections is dependent on the skill and knowledge of the stakeholders. To address this problem, this study proposes the Action Correction Recommendation (ACR) model to provide recommendations for action correction, using the Negative Association Rule mining technique. In addition to applying the association rule mining technique to build a High Defect Prediction Model (HDPM) to identify high defect action, the ACR builds a Low Defect Prediction Model (LDPM). For a submitted action, each HDPM rule used to predict the action as a high defect action and the LDPM rules are analyzed using negative association rule mining to spot the rule items with different characteristics in HDPM and LDPM rules. This information not only identifies the attributes required for corrections, but also provides a range (or a value) to facilitate the high defect action corrections. This study applies the ACR approach to a business software project to validate the efficiency of the proposed approach. The results show that the recommendations obtained can be applied to decrease software defect removal efforts.

Context Based Positive and Negative Spatio-Temporal Association Rule Mining

This paper proposes a new approach to mine context based positive and negative spatial association rules as they might be applied to hydrocarbon prospection. Many researchers are currently using an Apriori algorithm on spatial databases but this algorithm does not utilize the strengths of positive and negative association rules and of time series analysis, hence it misses the discovery of very interesting and useful associations present in the data. In dense spatial databases, the number of negative association rules is much higher compared to the positive rules which need exploitation. Using positive and negative association rule discovery and then pruning out the uninteresting rules consumes resources without much improvement in the overall accuracy of the knowledge discovery process. The associations among different objects and lattices are strongly dependent upon the context, particularly where context is the state of entity, environment or action. We propose an approach to spatial association rule mining from datasets projected at a temporal bar in which the contextual situation is considered while generating positive and negative frequent itemsets. An extended algorithm based on the Apriori approach is developed and compared with existing spatial association rule algorithms. The algorithm for positive and negative association rule mining is based on Apriori algorithm which is further extended to include context variable and simulate temporal series spatial inputs. The numerical evaluation shows that our algorithm is more efficient at generating specific, reliable and robust information than traditional algorithms.