Fuzzy Association Research Articles

Weighted fuzzy track association method based on Dempster–Shafer theory in distributed sensor networks

The uncertainty problem in sensor track to local track association is a difficult problem in distributed sensor networks, particularly when there is a big difference of sensors’ tracking performance. To solve this problem, a weighted fuzzy track association (FTA) method based on Dempster–Shafer theory is proposed. In the proposed method, five characteristics of sensor tracks from different sensors are established, and meanwhile their belief functions are defined to determine the corresponding beliefs. Considering the different effects of sensor tracks on track association, the reliabilities of sensor tracks are further presented and their magnitudes can be calculated by the combination belief function defined. Then, these reliabilities are used to reconstruct the fuzzy association degrees by the FTA method. The proposed method has an advantage that it can dynamically allocate the weight of each sensor track in association decision according to its characteristics. The performance of the proposed method is evaluated by using two experiments with simulation data in manoeuvring and uniform situations. It is found to be better than those of other two track association methods in tracking accuracy.

A Performance Model for FMCG Sector Employees Using Linguistic Fuzzy Multi Criteria Group Decision Making

Objectives: To propose a model for ranking the performance of the employees of FMCG sector. Methods/Statistical Analysis: In this paper, authors have used Min Max Method. The data for 100 employees have been generated for identifying the performance of employees through linguistic theory model. The main factors which affect the performance of the employee can be mainly categorized in four sections: job performance, job capacities, job behavior and personal. For each performance criterion the linguistic values has been assigned. Then the relative importance of linguistic variables is calculated. Findings: The major step in the decision making process is to establish a fuzzy association relation between a precise category and every employee's performance for that group. Considering the significance allocated to each criterion set the highest value for that grouping, the association relation is established by taking the complement of the category set importance. This complement produces a smallest performance value assigned to all employees as per the category. At the end, combined the performances of the employee across all groups in order to achieve a complete assessment by applying the min function to the set. Author got the results that 80 employees needed training in job performance as they have overall rating 0.2; whereas 16 employees needed training in job capacities as they have overall rating 0.40 and 4 employees can be promoted as they have overall rating 0.41 which is highest. Applications/Improvements: The proposed approach of fuzzy evaluation methodology can be used for other sectors like evaluation of students, evaluation of product or quality assessment of software, teacher or supplier evaluation with small modifications.

Futuristic data-driven scenario building: Incorporating text mining and fuzzy association rule mining into fuzzy cognitive map

Fuzzy cognitive maps (FCMs) are one of the representative techniques in developing scenarios that include future concepts and issues, as well as their causal relationships. The technique, initially dependent on deductive modeling of expert knowledge, suffered from inherent limitations of scope and subjectivity; though this lack has been partially addressed by the recent emergence of inductive modeling, the fact that inductive modeling uses a retrospective, historical data that often misses trend-breaking developments. Addressing this issue, the paper suggests the utilization of futuristic data, a collection of future-oriented opinions extracted from online communities of large participation, in scenario building. Because futuristic data is both large in scope and prospective in nature, we believe a methodology based on this particular data set addresses problems of subjectivity and myopia suffered by the previous modeling techniques. To this end, text mining (TM) and latent semantic analysis (LSA) algorithm are applied to extract scenario concepts from futuristic data in textual documents; and fuzzy association rule mining (FARM) technique is utilized to identify their causal weights based on if-then rules. To illustrate the utility of proposed approach, a case of electric vehicle is conducted. The suggested approach can improve the effectiveness and efficiency of scanning knowledge for scenario development.

Association rules mining based analysis of consequential alarm sequences in chemical processes

In the context of industrial alarm rationalization, the analysis of consequential alarms is helpful for finding out root alarms so as to avoid alarm flooding. Motivated by this idea, this paper introduces a weighted fuzzy association rules mining approach to discovering correlated alarm sequences. Combining fuzzy sets, Apriori algorithms and alarm time series analysis, the algorithm does not search the entire item sets to find out root causes of consequential alarms. Furthermore, by transforming the association rules into fuzzy-driven causal knowledge bases and establishing the compatible fuzzy inference mechanism, a rationalized alarm topology is eventually created. Experimental results of a chemical plant show that the novel approach taking advantage of fuzzy inferences and data mining strategies is potentially effective to remove redundant alarm sequences.

A Partial Weighted Utility Measure for Fuzzy Association Rule Mining

Background/Objectives: Association rules are generated from frequent item set by Association mining. The generation of frequent item set makes a great impact on decision making. The objective of the work is to introduce a new measure called SUF (Skill Utility Factor) to extract meaningful hidden item set and develop a hybrid algorithm FPWUM (Fuzzy Partial Weighted Utility Mining) for decision making. Methods/Statistical Analysis: The traditional measures support and confidence is augmented with SUF which can be useful for Human resource personnel to easily predict the work-force calibre in an organization. Using different methods like association rule mining, fuzzy logic and weighted utility mining has improved the prediction of attributes relations efficient and faster. Findings: The FPWUM extracts more efficient hidden frequent item sets through which many new and interesting rules are generated. Since the application of attribute’s weight are handled wisely and improvising factor is used only for hidden item set the model process time is reduced fairly. The idea of integrating the conventional measures and the SUF is a unique technique. The approach works well on real time dataset compared to the conventional models. The comparative result shows the algorithm’s ability. Improvements/ Applications: The algorithm uses predefined weighting scheme. It can be enhanced by using dynamic intelligent weighting factor.

Data Preprocessing and Cleansing in Web Log on Ontology for Enhanced Decision Making

Background/Objectives: Web applications are growing at a massive promptness and its users rise at exponential speed. The deviations in technology have made it potential to capture the user’s essence and interactions with web applications through web log file. Methods/Statistical Analysis: Due to huge amount of extraneous data in the web log, the original log file cannot be openly used in the Web Usage Mining (WUM) system. The web server log files used for mining several expedient patterns to analyze the access behavior of the user. Data pre-processing plays a dynamic role in Data Mining. Findings: We propose a new web log mining method for determining web access procedure from interpreted web usage logs which integrates data on user behaviors through self-rating and communication tracking. The raw data is preprocessed in order to improve the quality of data to be extracted. We discuss the significance of information pre-processing method and abundant steps elaborate in receiving the essential information effectively. This pre-processing method used to process and analysis the web log data for extraction of user search patterns. The fuzzy association rule minimizes users’ exploration period and facilitate for enhance decision making. Applications/Improvements: The proposed data cleaning method removes the extraneous records from web log. With this progression, we create a Personalized Ontology, which assists various semantic web applications such as site perfection, business intelligence and recommendations for behavioral analysis. Finally, we illustrate the efficiency of this method by the experimental results in the framework of data pre-processing and cleansing extraneous data for personalized ontology.

Application Of Fuzzy Multidimensional Association Rule For Analysis The Feasibility Of Granting Credit Plus To Prospective Customers ( Studi Kasus : PT. ”X ” Cabang Bangkinang )

PT. “X” Bangkinang branch in analyzing the feasibility of granting credit to prospective customers, are still using manual means with consideration of the results of the survey and the amount of salary prospects. With so many potential customers who apply that credit, the PT. “X ” Bangkinang branch having difficulty in making the right decision and quickly, therefor required an application that simultaneously can be used to processing the data, analyze the feasibility and send credit recommendations quickly, efficiently, and accurately. In this final, Fuzzy Association Rule is a method that is implemented for the recommendation whether or not potential customers were given credit by using several criteria which is, age, occupation, salary amount, character, security level salary and has a house or not, appropriate decisions based on rules recommended -rule or knowledge resulting from calculating the value of support, and confidence value of each first pattern grouped within a certain interval called the fuzzy sets. From the test results can be concluded that the method of fuzzy association rulemay result in the rules or knowledge to help the Credit analysts (CA) in decision-making prospects who deserve credit approval, but these systems do not have a dynamic menu that can add or reduce other supporting criteria for consideration of recommendations.

SPACS: Students’ Performance Analysis and Counseling System using Fuzzy logic and Association Rule Mining

Education plays an important role in one’s life if the right education and right environment is not provided then it somewhat distract the person from his/her path. In this regard, fuzzy expert system known as SPACS (Students’ Performance Analysis and Counselling System) is designed which helps to analyze the students’ academic performance by identifying all the critical factors and counsels him/her against those affecting parameters. In this paper, the study highlights on the critical factors that degrades the academic performance of the student based on the some interesting relationships between those factors. The performance of the overall system depends on the accuracy of the fuzzy knowledge base. Hence, association rule mining technique helps to generate the accurate results and also decreases the complexity of the system. General Terms Data mining, Expert System, quality education, academic performance, and fuzzy rule based expert system.

Mining fuzzy temporal association rules by item lifespans

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. In real-world applications, transactions may contain quantitative values and each item may have a lifespan from a temporal database. In this paper, we thus propose a data mining algorithm for deriving fuzzy temporal association rules. It first transforms each quantitative value into a fuzzy set using the given membership functions. Meanwhile, item lifespans are collected and recorded in a temporal information table through a transformation process. The algorithm then calculates the scalar cardinality of each linguistic term of each item. A mining process based on fuzzy counts and item lifespans is then performed to find fuzzy temporal association rules. Experiments are finally performed on two simulation datasets and the foodmart dataset to show the effectiveness and the efficiency of the proposed approach.

Fuzzy Translations of Fuzzy Associative Ideals in BCI-algebras

The aim of this paper is to introduce the concepts of fuzzy translation to fuzzy associative ideals in BCK/BCI-algebras. Also, the notion of fuzzy extensions and fuzzy multiplications of fuzzy associative ideals with several related properties are investigated. The relationships between fuzzy translations, fuzzy extensions and fuzzy multiplications of fuzzy ideals are investigated.

Fuzzy frameworks for mining data associations: fuzzy association rules and beyond

Looking for associations in data is one of the data mining tasks that has aroused more interest in the literature. In this area, incorporating concepts of fuzzy set theory is useful in problems where imprecision and/or uncertainty appear. In most of the existing approaches, fuzzy association rules are widely seen as fuzzy rules, which are very different in nature from association rules, so problems like fuzzy inclusion and cardinality have been seldom taken into account explicitly, mostly providing ad hoc solutions for capturing semantics. In contrast, in this study we have taken the more general and natural approach of considering the elements of the association rule mining framework and studying possible and sensible fuzzy extensions, referred here as fuzzy frameworks for mining associations. As fuzzy frameworks are abstract mathematical models, another key contribution of the article is the notion of interpretations as mappings between fuzzy frameworks and specific datasets. This general analysis of the field is completed with a study of various important aspects that arise when proposing quality measures in a fuzzy environment, as well as those related to computational issues. The work also includes a review of the fuzzy framework that arises from fuzzy transactions regarded as fuzzy subsets of items, and shows that many of the approaches on fuzzy association rules that exist in the literature can be placed in the context of a proper interpretation of that framework. WIREs Data Mining Knowl Discov 2016, 6:50–69. doi: 10.1002/widm.1176This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Association Rules

A Novel Approach for Discovery Quantitative Fuzzy Multi-Level Association Rules Mining Using Genetic Algorithm

Quantitative multilevel association rules mining is a central field to realize motivating associations among data components with multiple levels abstractions. The problem of expanding procedures to handle quantitative data has been attracting the attention of many researchers. The algorithms regularly discretize the attribute fields into sharp intervals, and then implement uncomplicated algorithms established for Boolean attributes. Fuzzy association rules mining approaches are intended to defeat such shortcomings based on the fuzzy set theory. Furthermore, most of the current algorithms in the direction of this topic are based on very tiring search methods to govern the ideal support and confidence thresholds that agonize from risky computational cost in searching association rules. To accelerate quantitative multilevel association rules searching and escape the extreme computation, in this paper, we propose a new genetic-based method with significant innovation to determine threshold values for frequent item sets. In this approach, a sophisticated coding method is settled, and the qualified confidence is employed as the fitness function. With the genetic algorithm, a comprehensive search can be achieved and system automation is applied, because our model does not need the user-specified threshold of minimum support. Experiment results indicate that the recommended algorithm can powerfully generate non-redundant fuzzy multilevel association rules.

A cloud-based location assignment system for packaged food allocation in e-fulfillment warehouse

E-retailing is the delivery of products and services over the Internet, and it has been growing rapidly in recent years. Logistics services in the related warehouses have to be enhanced accordingly in order to handle the huge amount of e-orders in a shorter timeframe. Moreover, since the e-retailing of packaged food has been getting popular, these warehouses inevitably have to handle food products requiring special care, including temperature, humidity and insolation control, and expiry date management. Consequently, suitable storage guidelines have to be formulated for these warehouses to cope with their more demanding daily operations. A cloud-based location assignment system (CLAS) is thus proposed in this article, which provides timely and comprehensive solutions to the storage location assignment problem for warehouse operators. CLAS applies (i) cloud-based infrastructure for enhancing the agility of the proposed system, (ii) fuzzy association rule mining for predicting the potential storage time of various types of packaged food, and (iii) fuzzy logic and association rule mining for offering the most preferred storage locations of various products. A prototype of the CLAS is tested in a warehouse which involves packaged food handling. The test results show that the operational efficiency measured in terms of storage assignment decision-making time and order-picking time and the quality of the storage assignment solutions measured in terms of product disposal rate and product return/exchange rate have been improved in the case company.

An Analysis of Fuzzy Weighted Association Rule Mining from Medical Data

Feature selection and classification techniques are the important factors required for the decision making process in Data mining. Recent developments show that fuzzy association rule mining algorithms took the place of conventional classification techniques because of the efficiency of the algorithm. However, fuzzy association rule mining algorithms suffers from the exponential growth of rules produced by fuzzy partitioning of attributes. An attempt is made to propose three different fuzzy weighted association rule mining algorithms. The proposed algorithm incorporates the concept of information gain, gain ratio and correlation value combined with the gain ratio features selection methods. Also a comparative analysis of the proposed algorithms is done with the existing fuzzy association rule mining and it is shown that the classification accuracy value is better for the proposed approaches. The performance has been analyzed with benchmark data collected from the UCI repository.

On the mining of fuzzy association rule using multi-objective genetic algorithms

The discovery of association rule acquire an imperative role in data mining since its inception, which tries to find correlation among the attributes in a database. Classical algorithms/procedures meant for Boolean data and they suffer from sharp boundary problem in handling quantitative data. Thereby fuzzy association rule (i.e., association rule based on fuzzy sets) with fuzzy minimum support and confidence is introduced as an alternative tool. Besides, rule length, comprehensibility, and interestingness are also potentially used as quality metrics. Additionally, in fuzzy association rule mining, determining number fuzzy sets, tuning membership functions and automatic design of fuzzy sets are prominent objectives. Hence fuzzy association rule mining problem can be viewed as a multi-objective optimisation problem. On the other side, multi-objective genetic algorithms are established and efficient techniques to uncover Pareto front. Therefore, to bridge these two fields of research many methods have been developed. In this paper, we present some of the popular state-of-art multi-objective fuzzy-genetic algorithms for mining association rules. In addition, their novelty, strengths, and weaknesses have been analysed properly with a comparative performance. The indicative future research direction and an extensive bibliography of this paper may be an attracting point for researchers from diversified domains to explore and exploit further.

A novel denitration cost optimization system for power unit boilers

Reducing the denitration cost of coal-fired boilers is important to enhance the competitiveness of power generation companies. This study proposes a real operation data-based denitration cost optimization system that guides operators in economically adjusting the operation parameters of boilers. A data-driven least square support vector machine (LSSVM) learning method is utilized to predict the denitration cost of a coal-fired boiler. Back propagation (BP) is used here to select the input variables to simplify the model. With the pre-built BP-LSSVM-based denitration cost model, the genetic algorithm (GA) is then applied to obtain offline optimizations at the typical operating load points, which results in an Offline Optimal Expert Database (OOED). Once a load command is received, fuzzy association rule mining (FARM) is employed to extract the relationship between the operating load point and the optimal adjustable variables (AVs) in the OOED, thereby achieving the online denitration cost optimization of the power plant. For comparison, a single LSSVM method is also employed to build a denitration cost prediction model, and the GA and FARM proposed in this study are compared too. The results show that, compared with the single LSSVM method, the BP-LSSVM method not only predicts more accurately but also lowers the model complexity. In addition, considering the denitration cost, optimization time, and update time, the proposed BP-LSSVM-GA-FARM-based denitration cost optimization system is always better than traditional optimization methods.

ECG-based Mental Stress Assessment Using Fuzzy Computing and Associative Petri Net

Medical reports suggest that long-term stress can directly or indirectly lead to mental disorders and cardiovascular disease. However, many people frequently ignore their stress symptoms and fail to take remedial action before developing serious psychological or physiological health problems. Many previous studies have used electrocardiograms (ECG) to evaluate mental stress. However, ECG pattern recognition presents difficulties because the time-varying morphology is subject to physiological conditions and the presence of noise. Therefore, how to achieve effective noise reduction and accurately determine an individual’s exposure to mental stress under various conditions is a recurring issue in engineering and medical research. Heart rate variability is assessed using time- and frequency-domain analyses. To accurately detect mental stress, this research adopts both time and frequency domains, two aspects of physiological characteristics. In this study, a rule-based reasoning model is created for mental stress assessment by combining fuzzy and associative Petri net methodologies. It can serve as a basis for clinical diagnosis and quickly measure the subject’s mental condition and reduce subjective errors. Performance evaluation using Physionet’s stress recognition database shows that the proposed approach compares well with data mining methods and other proposed methods. Moreover, a prototype mental stress assessment system is proposed to help users understand their mental condition.

Fuzzy Rule-Based Classification Systems for multi-class problems using binary decomposition strategies: On the influence of n-dimensional overlap functions in the Fuzzy Reasoning Method

Multi-class classification problems appear in a broad variety of real-world problems, e.g., medicine, genomics, bioinformatics, or computer vision. In this context, decomposition strategies are useful to increase the classification performance of classifiers. For this reason, in a previous work we proposed to improve the performance of FARC-HD (Fuzzy Association Rule-based Classification model for High-Dimensional problems) fuzzy classifier using One-vs-One (OVO) and One-vs-All (OVA) decomposition strategies. As a result of an exhaustive experimental analysis, we concluded that even though the usage of decomposition strategies was worth to be considered, further improvements could be achieved by introducing n-dimensional overlap functions instead of the product t-norm in the Fuzzy Reasoning Method (FRM). In this way, we can improve confidences for the subsequent processing performed in both OVO and OVA.In this paper, we want to conduct a broader study of the influence of the usage of n-dimensional overlap functions to model the conjunction in several Fuzzy Rule-Based Classification Systems (FRBCSs) in order to enhance their performance in multi-class classification problems applying decomposition techniques. To do so, we adapt the FRM of four well-known FRBCSs (CHI, SLAVE, FURIA, and FARC-HD itself). We will show that the benefits of the usage of n-dimensional overlap functions strongly depend on both the learning algorithm and the rule structure of each classifier, which explains why FARC-HD is the most suitable one for the usage of these functions.

Finding Active Membership Functions for Genetic-Fuzzy Data Mining

Since transactions may contain quantitative values, many approaches have been proposed to derive membership functions for mining fuzzy association rules using genetic algorithms (GAs), a process known as genetic-fuzzy data mining. However, existing approaches assume that the number of linguistic terms is predefined. Thus, this study proposes a genetic-fuzzy mining approach for extracting an appropriate number of linguistic terms and their membership functions used in fuzzy data mining for the given items. The proposed algorithm adjusts membership functions using GAs and then uses them to fuzzify the quantitative transactions. Each individual in the population represents a possible set of membership functions for the items and is divided into two parts, control genes (CGs) and parametric genes (PGs). CGs are encoded into binary strings and used to determine whether membership functions are active. Each set of membership functions for an item is encoded as PGs with real-number schema. In addition, seven fitness functions are proposed, each of which is used to evaluate the goodness of the obtained membership functions and used as the evolutionary criteria in GA. After the GA process terminates, a better set of association rules with a suitable set of membership functions is obtained. Experiments are made to show the effectiveness of the proposed approach.

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.