Fuzzy Association Rule Mining Research Articles

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.

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

Genetic learning of the membership functions for mining fuzzy association rules from low quality data

Many methods have been proposed to mine fuzzy association rules from databases with crisp values in order to help decision-makers make good decisions and tackle new types of problems. However, most real-world problems present a certain degree of imprecision. Various studies have been proposed to mine fuzzy association rules from imprecise data but they assume that the membership functions are known in advance and it is not an easy task to know a priori the most appropriate fuzzy sets to cover the domains of the variables. In this paper, we propose FARLAT-LQD, a new fuzzy data-mining algorithm to obtain both suitable membership functions and useful fuzzy association rules from databases with a wide range of types of uncertain data. To accomplish this, first we perform a genetic learning of the membership functions based on the 3-tuples linguistic representation model to reduce the search space and to learn the most adequate context for each fuzzy partition, maximizing the fuzzy supports and the interpretability measure GM3M in order to preserve the semantic interpretability of the obtained membership functions. Moreover, we propose a new algorithm based on the Fuzzy Frequent Pattern-growth algorithm, called FFP-growth-LQD, to efficiently mine the fuzzy association rules from inaccurate data considering the learned membership functions in the genetic process. The results obtained over 3 databases of different sizes and kinds of imprecisions demonstrate the effectiveness of the proposed algorithm.

Mining Fuzzy Association Rules Based on Parallel Particle Swarm Optimization Algorithm

The association rule extraction process often involves a large number of candidate item sets and multiple read operations on data sets. With the emergence of massive data, the sequential association rule extraction algorithm also suffers from large I/O overhead and insufficient memory. This paper presents a new multi-swarm parallel multi-mutation particle swarm optimization algorithm (MsP-MmPSO) to search several groups in parallel. Experimental results show that the MsP-MmPSO algorithm has an advantage in terms of execution time over traditional particle swarm optimization, especially when the amount or dimensions of the data increase. Experiments also verify that a good task allocation method can reduce the execution time of the parallel algorithm.

Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

Prediction of high incidence of dengue in the Philippines.

BackgroundAccurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines.MethodsModel inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data.Principal FindingsModel accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation.ConclusionsThis method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and Specificity.

A genetic fuzzy system for unstable angina risk assessment

BackgroundUnstable Angina (UA) is widely accepted as a critical phase of coronary heart disease with patients exhibiting widely varying risks. Early risk assessment of UA is at the center of the management program, which allows physicians to categorize patients according to the clinical characteristics and stratification of risk and different prognosis. Although many prognostic models have been widely used for UA risk assessment in clinical practice, a number of studies have highlighted possible shortcomings. One serious drawback is that existing models lack the ability to deal with the intrinsic uncertainty about the variables utilized.MethodsIn order to help physicians refine knowledge for the stratification of UA risk with respect to vagueness in information, this paper develops an intelligent system combining genetic algorithm and fuzzy association rule mining. In detail, it models the input information’s vagueness through fuzzy sets, and then applies a genetic fuzzy system on the acquired fuzzy sets to extract the fuzzy rule set for the problem of UA risk assessment.ResultsThe proposed system is evaluated using a real data-set collected from the cardiology department of a Chinese hospital, which consists of 54 patient cases. 9 numerical patient features and 17 categorical patient features that appear in the data-set are selected in the experiments. The proposed system made the same decisions as the physician in 46 (out of a total of 54) tested cases (85.2%).ConclusionsBy comparing the results that are obtained through the proposed system with those resulting from the physician’s decision, it has been found that the developed model is highly reflective of reality. The proposed system could be used for educational purposes, and with further improvements, could assist and guide young physicians in their daily work.

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.

Using Fuzzy Association Rules to Design E-commerce Personalized Recommendation System

In order to improve the efficiency of fuzzy association rule mining, the paper defines the redundant fuzzy association rules, and strong fuzzy association rules redundant nature. As much as possible for more information in the e-commerce environment, and in the right form is a prerequisite for personalized recommendation. Personalized recommendation technology is a core issue of e-commerce automated recommendation system. Higher complexity than ordinary association rules algorithm fuzzy association rules, the low efficiency become a bottleneck in the practical application of fuzzy association rules algorithm. The paper presents using fuzzy association rules to design E-commerce personalized recommendation system. The experimental results show that the new algorithm to improve the efficiency of the implementation. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3983

A Survey of Fuzzy Based Association Rule Mining to Find CoOccurrence Relationships

Data mining is the analysis step of the Knowledge Discovery in Databases process, or KDD. It is the process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and transform it into a human-understandable structure.In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. Association rules are usually required to satisfy a user-specified minimum support and a user-specified minimum confidence at the same time. A fuzzy association rule mining (firstly expressed as quantitative association rule mining) has been proposed using fuzzy sets such that quantitative and categorical attributes can be handled. A fuzzy rule represents each item as pair. Fuzzy logic softens the effect of sharp boundary intervals and solves the problem of uncertainty present in data relationships.In this paper we represent a survey of Association Rule Mining Using Fuzzy Algorithm. The techniques are categorized based upon different approaches. This paper provides the major advancement in the approaches for association rule mining using fuzzy algorithms.

Fuzzy temporal association rules: combining temporal and quantitative data to increase rule expressiveness

Data mining for association rules aims to discover interesting relationships among sets of items in a database. Very often these databases include some kind of temporal information, the most common being a temporal label indicating transaction date. Within the field of association rule mining temporal information has been used to obtain sequential association rules, periodic or cyclic association rules, calendric association rules, or event‐driven association rules. The temporal component is also relevant when analyzing how association rules evolve if datasets are evaluated on different time‐slices. On the other hand, in traditional association rules item attributes were usually Boolean, but many attributes in current databases are quantitative in nature. Fuzzy temporal association rules arise from the use of fuzzy sets to describe quantitative temporal and/or not temporal attributes of items in a database, and/or to introduce fuzzy temporal specifications for the rules a user is interested in; the use of fuzzy sets allows a linguistic interpretation of the rules and also provides means to handle the uncertainty present in attribute measurements. Depending on the rule pattern the final user is interested in, different methods for fuzzy temporal association rule mining can be found in the literature, with mining algorithms adapted to the rule model being used. WIREs Data Mining Knowl Discov 2014, 4:64–70. doi: 10.1002/widm.1116This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Technologies > Association Rules Technologies > Computational Intelligence

A RFID-based recursive process mining system for quality assurance in the garment industry

With the increasing concern about product quality, attention has shifted to the monitoring of production processes to be assured of good quality. Achieving good quality is a challenging task in the garment industry due to the great complexity of garment products. This paper presents an intelligent system, using fuzzy association rule mining with a recursive process mining algorithm, to find the relationships between production process parameters and product quality. The goal is to derive a set of decision rules for fuzzy logic that will determine the quantitative values of the process parameters. Learnt process parameters used in production form new inputs of the initial step of the mining algorithm so that new sets of rules can be obtained recursively. Radio frequency identification technology is deployed to increase the efficiency of the system. With the recursive characteristics of the system, process parameters can be continually refined for the purpose of achieving quality assurance. A case study is described in which the system is applied in a garment manufacturing company. After a six-month pilot run of the system, the numbers of critical defects, major defects and minor defects were reduced by 7, 20 and 24%, respectively while production time and rework cost improved by 26 and 30%, respectively. Results demonstrate the practical viability of the system to provide decision support for garment manufacturers who may not be able to determine the appropriate process settings for achieving the desired product quality.

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.

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Abstract A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method’s ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.

Web usage mining with evolutionary extraction of temporal fuzzy association rules

In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets’ boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules.

Fuzzy association rule mining approaches for enhancing prediction performance

This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM–Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) that can be used in prediction of a future value. The knowledge extraction process and the performance of the model are demonstrated through two case studies of road traffic data sets with different sizes. The experimental results show the merits and capability of the proposed KD model in FARs based knowledge extraction. The second model (the FCM–MSapriori model) integrates FCM and a Multiple Support Apriori (MSapriori) approach to extract the FARs. These FARs provide the knowledge base to be utilized within the FIS for prediction evaluation. Experimental results have shown that the FCM–MSapriori model predicted the future values effectively and outperformed the FCM–Apriori model and other models reported in the literature.

FARP: Mining fuzzy association rules from a probabilistic quantitative database

Current studies on association rule mining focus on finding Boolean/quantitative association rules from certain databases or Boolean association rules from probabilistic databases. However, little work on mining association rules from probabilistic quantitative databases has been mentioned because the simultaneous measurement of quantitative information and probability is difficult. By introducing a novel Shannon-like Entropy, we aggregate and measure the information contained in an item with the coexistence of fuzzy uncertainty hidden in quantitative values and random uncertainty. We then propose Support and Confidence metrics for a fuzzy–probabilistic database to quantify association rules. Finally, we design an algorithm, called FARP (mining Fuzzy Association Rules from a Probabilistic quantitative data), to discover frequent fuzzy–probabilistic itemsets and fuzzy association rules using the proposed interest measures. The experimental results show the effectiveness of our method and its practicality in real-world applications.

A Survey on Fuzzy Association Rule Mining Methodologies

Abstract Fuzzy association rule mining (Fuzzy ARM) uses fuzzy logic to generate interesting association : rules. These association relationships can help in decision making for the solution of a given problem. Fuzzy ARM is a variant of classical association rule mining. Classical association rule mining uses the concept of crisp sets. Because of this reason classical association rule mining has several drawbacks. To overcome those drawbacks the concept of fuzzy association rule mining came. Today there is a huge number of different types of fuzzy association rule mining algorithms are present in research works and day by day these algorithms are getting better. But as the problem domain is also becoming more complex in nature, continuous research work is still going on. In this paper, we have studied several well-known methodologies and algorithms for fuzzy association rule mining. Four important methodologies are briefly discussed in this paper which will show the recent trends and future scope of research in the field of fuzzy association rule mining.

A Survey on Fuzzy Association Rule Mining

Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve the aforesaid problem by handling quantitative data using fuzzy set. In this paper, the authors present an updated survey of fuzzy association rule mining procedures along with a discussion and relevant pointers for further research.