Fuzzy Association Rules Research Articles

A slippery genetic algorithm-based process mining system for achieving better quality assurance in the garment industry

Due to the error-prone nature of garment manufacturing operations, it is challenging to guarantee the quality of garments. Previous research has been done to apply fuzzy association rule mining to determine process settings for improving the garment quality. The relationship between process parameters and the finished quality is represented in terms of rules. This paper enhances the application by encoding the rules into variable-length chromosomes for optimization with the use of a novel genetic algorithm (GA), namely the slippery genetic algorithm (sGA). Inspired by the biological slippage phenomenon in DNA replication, sGA allows changes to the chromosome lengths by insertion and deletion. During rule optimization, different parameters can be inserted to or removed from a rule, increasing the diversity of the solutions. In this paper, a slippery genetic algorithm-based process mining system (sGAPMS) is developed to optimize fuzzy rules with the aim of facilitating a comprehensive quality assurance scheme in the garment industry. The significance of this paper includes the development of a novel variable-length GA mechanism and the hybridization of fuzzy association rule mining and variable-length GAs. Though the capability of conventional GA in rule optimization has been proven, the diversity in the population is inherently limited by the fixed chromosome length. Motivated by this phenomenon, the sGA suggested in this paper allows various parameters to be considered in a rule, improving the diversity of the solutions. A case study is conducted in a garment manufacturing company to evaluate the sGAPMS. The results illustrate that better quality assurance can be achieved after rule optimization.

Correlated gain ratio based fuzzy weighted association rule mining classifier for diagnosis health care data

Healthcare data need an accurate diagnosis of diseases with the low computation time. Fuzzy association rule mining converts quantitative attributes to fuzzy attributes which maintain the integrity of information. Fuzzy association rule mining is most effective among various classification methods used for diagnosing health care data. The major challenge in the fuzzy association rule mining is to reduce the exponential growth of rules produced by fuzzy partitioning of attributes. The proposed method uses the correlation and gain ratio based average ranking feature selection followed by fuzzy weighted association rule mining classifier to diagnose the medical data set. The average ranking feature selection method improves classification accuracy and reduces the number of rules by ranking the proper potential attribute. The computation time is also minimized by reducing number of rules. The performance of fuzzy weighted association rule mining classifiers based on correlation and gain ratio average ranking feature selection is evaluated by comparing the classification accuracy with three classifiers and five benchmark data set collected from UCI repository.

Mining Fuzzy Association Rules in the Framework of AFS Theory

In this paper, firstly we study the representations and fuzzy logic operations for the fuzzy concepts in real data systems. Secondly, we propose a new fuzzy association rule mining algorithm in the framework of AFS (Axiomatic Fuzzy Sets) theory. Compared with the current algorithms, the advantage of proposed algorithm has two advantages. One is that the membership functions of the fuzzy sets representing the extracted rules and the fuzzy logic operations applied to extract fuzzy rules are determined by the distribution of the data, instead of the fuzzy sets defined by some special functions, t-norm, t-conorm, negation operator, implication operator and fuzzy similarity relation given in advance. The extracted fuzzy rules are interpretable and similar to human intuition. Another is that its simplicity in implementation and mathematical beauty in fuzzy theory, and can be directly applied to extract fuzzy association rules in real data systems. Finally, a well-known example Iris dataset is used to illustrate the effectiveness of the new algorithm based on the proposed degrees of implication. We obtained reclassification accuracy 98 %.

An Atanassov’s intuitionistic fuzzy reasoning model

The task of the standard Mamdani fuzzy logic controller is to find a crisp control action from the fuzzy rule-base and from a set of crisp inputs. In this paper we modify this controller in order to work with Atanassov’s intuitionistic fuzzy sets and to activate a set of rules having the same concl usion. Usually, the inference rules used in a fuzzy logic controller are given by a domain expert; in our system, these rules are automatically induced as fuzzy association rules starting from a training set. The fuzzy confidence value associated with each rule is used to obtain the fuzzy set inferred by our system.

Privacy Clustered Mining of Association Rulesin Distributed Database by UsingFuzzyfication

Data Mining is very emerging technology for mining efficient dataset according to the user request. Association rules are very crucial part in data mining which tell the relationship between two item sets by using the apriori algorithm so that it will give the frequent item sets. Algorithm used in the system is EDMA (Efficient Data Mining Algorithm) which processes the user query and navigates to the appropriate databases which are local to that site. In First step apriori algorithm is used to find the association of requested dataset and in second step the fuzzy association rules are taken place by applying the fuzzy operations. The system uses the fuzzy association so that if any item does not get the association with any other item then it will be associated because of the complex fuzzy operations. So that it will be better to get the requested data to the user because the system will check the datasets on each and every site and by combining the datasets of different sites it will be given to the user. Thus the transaction will be secured by using the association key and also user requested data will be given and also the system is used as a analytical tool which provides the precise data.

Fuzzy association rule mining and classification for the prediction of malaria in South Korea.

BackgroundMalaria is the world’s most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality.MethodsWe describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as LOW, MEDIUM or HIGH, where these classes are defined as a total of 0–2, 3–16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak.ResultsModel accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7–8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the MEDIUM class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3.ConclusionsA previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict LOW, MEDIUM or HIGH cases 7–8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.

Efficient mining fuzzy association rules from ubiquitous data streams

Due to the development in technology, a number of applications such as smart mobile phone, sensor networks and GPS devices produce huge amount of ubiquitous data in the form of streams. Different from data in traditional static databases, ubiquitous data streams typically arrive continuously in high speed with huge amount, and changing data distribution. Dealing with and extracting useful information from that data is a real challenge. This raises new issues, that need to be considered when developing association rule mining techniques for these data. It should be noted, that data, in the real world, are not represented in binary and numeric forms only, but it may be represented in quantitative values. Thus, using fuzzy sets will be very suitable to handle these values.In this paper the problem of mining fuzzy association rules from ubiquitous data streams is studied, and a novel technique FFP_USTREAM (Fuzzy Frequent Pattern Ubiquitous Streams) is developed. This technique integrates fuzzy concepts with ubiquitous data streams, employing sliding window approach, to mine fuzzy association rules. In addition, the complexity and the efficiency of this technique are discussed. Examples of real data sets are used to test the proposed technique. Further research issues are also suggested.

Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills

The integration of association rules and correlation rules with fuzzy logic can produce more abstract and flexible patterns for many real life problems, since many quantitative features in real world, especially surveying the frequency of plant association in any region is fuzzy in nature. This paper presents a modification of a previously reported algorithm for mining fuzzy association and correlation rules, defines the concept of fuzzy partial and semi-partial correlation rule mining, and presents an original algorithm for mining fuzzy data based on correlation rule mining. It adds a regression model to the procedure for mining fuzzy correlation rules in order to predict one data instance from contributing more than others. It also utilizes statistical analysis for the data and the experimental results show a very high utility of fuzzy association rules and fuzzy correlation rule mining in modeling plant association problems. The newly proposed algorithm is utilized for seeking close associations and relationships between a group of plant species clustering around Sandalwood in Pachaimalai hills, Eastern Ghats, Tamilnadu.

Provide A New Approach for Mining Fuzzy Association Rules using Apriori Algorithm

Association rules mining is one of the most popular data mining models. Minimum-support is used in association rules mining algorithms, like Apriori, FP-Growth, Eclat and etc. One problem Apriori algorithm and other algorithms in the field association rules mining, this is user must determine the threshold minimum-support. Suppose that the user wants to apply Apriori algorithm on a database with millions of transactions, definitely user can not have the necessary knowledge about all the transactions in the database, and therefore would not be able to determine an appropriate threshold. In this paper, using averaging techniques, we propose a method in which Apriori algorithm would specify the minimum support in a fully automated manner. Our goal in this paper improved algorithm Apriori, to achieve it, initially will try to use fuzzy logic to distribute data in different clusters, and then we try to introduce the user the most appropriate threshold automatically. The results show that this approach causes the any rule which can be interesting will not be lost and also any rule that is useless cannot be extracted. The simulation results on a real example show that our approach works better than the classic algorithms.

Provide a New Approach for Mining Fuzzy Association Rules using Apriori Algorithm

Provide a New Approach for Mining Fuzzy Association Rules using Apriori Algorithm

Fuzzy Association Rule Mining Based Myocardial Ischemia Diagnosis on ECG Signal

A fuzzy association rule mining based method is proposed for myocardial ischemia diagnosis on ECG signals. The proposal provides interpretable and understandable information to doctors as an assistant reference, while rule mining on fuzzy itemsets guarantees that the feature segmentation before rule extraction is feasible and effective. A set of fuzzy association rules is mined through experiments on data from the European ST-T Database, and classification results of myocardial ischemia and normal heartbeats on the test dataset using the extracted rules obtained values of 83.4%, 80.7%, and 81.4% for sensitivity, specificity, and accuracy, respectively. The proposed method aims to become a helpful tool to accelerate the diagnosis of myocardial ischemia on ECG signal, and to be expanded to other heart disease diagnosis areas such as hypertensive heart disease and arrhythmia.

Fuzzy association rule mining for fashion product development

Purpose – The emergence of the fast fashion trend has exerted a great pressure on fashion designers who are urged to consider customers’ preferences in their designs and develop new products in an efficient manner. The purpose of this paper is to develop a fuzzy association rule mining (FARM) approach for improving the efficiency and effectiveness of new product development (NPD) in fast fashion. Design/methodology/approach – The FARM identifies the hidden relationships between product styles and customer preferences. The knowledge discovered help the fashion industry design new products which are not only fashionable, but are also saleable in the market. Findings – To evaluate the proposed approach, a case study is conducted in a Hong Kong-based fashion company in which a real-set of data are tested to generate fuzzy association rules. The results reveal that the FARM approach can provide knowledge support to the fashion industry during NPD, shorten the NPD cycle time, and increase customer satisfaction. Originality/value – Compared with traditional association rule mining, the proposed FARM approach takes the fuzziness of data into consideration and the knowledge represented in the fuzzy rules is in a more human-understandable structure. It captures the voice of the customer into fashion product development and provides a specific solution to deal with the challenges brought by fast fashion. In addition, it helps increase the innovation and technological capability of the fashion industry.

Mining fuzzy amino acid associations in peptide sequences of mycobacterium tuberculosis complex (MTBC)

The biological databases are flooded with genomic and proteomic data which can be analyzed to generate the information and knowledge which can be useful for understanding molecular mechanisms involved in disease and health state of a living being. The tuberculosis is an infectious disease and is pandemic, causing large number of deaths every year. In this paper an attempt has been made to develop a model for mining amino acid association patterns in peptide sequences of MTBC. The peptide sequences of species of MTBC are taken from the NCBI. The variation in the length of these sequences leads to variation in degree of relationship among amino acids present in each sequence. The fuzzy set is employed to model this uncertainty of degree of relationships among the amino acids of the peptide sequences of MTBC. The crisp and fuzzy amino acid association rules have been generated from the peptide sequences of MTBC and on comparison it is observed that fuzzy set approach is able to address the issue of under prediction and over prediction of amino acid association patterns due to uncertainty in degree of relationship among the amino acid. The amino acid association patterns have been used to predict secondary structure and physiochemical properties as an illustration. Thus the patterns generated can be useful in understanding the molecular mechanisms involved in MTBC by predicting physiochemical properties, structures and protein–protein interactions etc.

Improve efficiency of fuzzy association rule using hedge algebra approach

A major problem when conducting mining fuzzy association rules from the database (DB) is the large computation time and memory needed. In addition, the selection of fuzzy sets for each attribute of the database is very important because it will aect the quality of the mining rule. This paper proposes a method for mining fuzzy association rules using compressed database. We also use the approach of Hedge Algebra (HA) to build the membership function for attributes instead of using the normal way of fuzzy set theory. This approach allows us to explore fuzzy association rules through a relatively simple algorithm which is faster in terms of time, but it still brings association rules which are as good as the classical algorithms for mining association rules.

The Application of Fuzzy Association Rule on Co-movement Analyze of Indonesian Stock Price

Abstract One type of investments in securities is stock as a sign of ownership for individual or corporation within a company. For making investment strategy and portfolio, investors should pay more attention to the co-movement of the stock price. Several methods can be used to see moving of the stock price, one of them is data mining. In this paper, association rule mining algorithm is used to find the stock movement relationship of inter-company in Indonesia. Fuzzy approach is used as tools to categorize data based on the time and transactions sequence. Experimental results show that association rule method can solve the problems. By using some values of minimum support, some strong association rules among companies can be obtained.

Fuzzy Association Rule Mining

The paper attempts to propose a fuzzy logic association algorithm to predict the risks involved in identifying diseases like breast cancer. Fuzzy logic algorithm is used to find association rules. The results of the study revealed that the prediction is better reliable than conventional methods.

Mining amino acid association patterns in class B GPCRs.

Class B GPCR family is a small group of receptors which are activated by peptides of intermediate length that range from 30 to 40 amino acid residues including hormones, neuropeptides and autocrine factors that mediate diverse physiological functions. They are involved in physiological processes like glucose homeostasis (glucagon and glucagon-like peptide-1), calcium homeostasis and bone turnover (parathyroid hormone and calcitonin), and control of the stress axis (corticotropin-releasing factor). Most of the GPCR structures and their functions are still unknown. Thus, the study of amino acid association patterns can be useful in prediction of their structure and functions. In view of above, in this paper, an attempt has been made to explore amino acid association patterns in class B GPCRs and their relationships with secondary structures and physiochemical properties. The fuzzy association rule mining is employed to take care of uncertainty due to variation in length of sequences. The association rules have been generated with the help of patterns discovered in the sequences.

A Collaborative Web Recommendation System Based on Fuzzy Association Rule Mining Techniques

Currently the use of web recommendation techniques is growing worldwide with aim of providing the custom-made required data to end users. Most of the web users complain about finding useful information on web sites. Web recommender systems predict the information needs of users and provide them with recommendations to facilitate their navigation. Recommender systems have been extensively explored in web mining. The web recommendation systems are split into two main categories such as collaborative recommendation system and content based recommendation system. However, the quality of recommendations and the user satisfaction with such systems are still not optimal. In adaptive association recommender systems based on web mining techniques have strengths and weaknesses. One of these weaknesses is lack of consideration of an appropriate measure to calculate the user’s interest degree of pages. Applying Fuzzy Association Rule Mining (FARM) algorithm to a recommender system leads to calculate user’s interest of pages more accurately. Consequently generated recommendations will be more desirable when compared to other association rule mining techniques.

A CMFFP-tree algorithm to mine complete multiple fuzzy frequent itemsets

In the past, many algorithms were proposed to adopt fuzzy-set theory for discovering fuzzy association rules from quantitative databases. The fuzzy frequent pattern (FFP)-tree and the compressed fuzzy frequent pattern (CFFP)-tree algorithms were respectively proposed to mine the incomplete fuzzy frequent itemsets from the tree-based structures. In the past, multiple fuzzy frequent pattern (MFFP)-tree algorithm was proposed to keep more linguistic terms for mining fuzzy frequent itemsets. Since the MFFP-tree algorithm inherits the property of the FFP-tree algorithm, numerous tree nodes are thus required to build the MFFP-tree structure for mining the desired multiple fuzzy frequent itemsets. In this paper, the compressed multiple fuzzy frequent pattern (CMFFP)-tree algorithm is designed to keep not only the linguistic term with maximum membership value but also the other frequent linguistic terms for mining the completely fuzzy frequent itemsets. In the designed CMFFP-tree algorithm, the multiple frequent linguistic terms are sorted in descending order of their occurrence frequencies to build the CMFFP-tree structure. The construction process is the same as the CFFP-tree algorithm except more information are kept for later mining process to discover the completely fuzzy frequent itemsets. Each node in the CMFFP-tree uses the additional array to keep the membership values of its prefix path by intersection operation. A CMFFP-mine algorithm is also designed to efficiently mine the multiple fuzzy frequent itemsets from the developed CMFFP-tree structure. Experiments are then conducted to show the performance of the proposed CMFFP-tree algorithm in terms of execution time and the number of tree nodes, compared to those of the MFFP-tree and CFFP-tree algorithms.

Quality measures for fuzzy predicates in conjunctive and disjunctive normal forms

Association rule mining is a very popular data mining technique. Rules in this technique are often used to identify and represent dependencies between attributes in databases. Specifically, fuzzy association rules are rules that use the concepts of fuzzy sets and can be considered as a special case of fuzzy predicates. Many quality measures have been defined for fuzzy association rules, but all consider a specific structure: antecedent and consequence. In the case of fuzzy predicates in the normal form (i.e., conjunctive or disjunctive), it is necessary to define different quality measures that do not consider the structure as an antecedent or a consequence. The only available measure for this scenario is the fuzzy predicate truth value (FPTV), which has serious limitations. The evaluation of fuzzy predicates in the normal form through appropriate quality measures has not yet been clearly defined in the literature. Thus, we propose several quality measures specifically for fuzzy predicates in the conjunctive (CNF) and disjunctive (DNF) normal forms. Experimental studies illustrate the use of the proposed measures and allow some general conclusions about each measure.