Fuzzy Association Rule Mining Algorithm Research Articles

Analysis on the Establishment and Management of Library Resource Base Based on Modern Information Technology

The current rapid development of modern network information technology, along with the arrival of this trend, has profoundly changed people’s living and learning methods. In the past, the management of book resources in the traditional period has gradually exposed many drawbacks, and it is difficult to meet the technological development of the information age. Combined with the complexity of book management parameters, high variable dimensions, and the characteristics of multiattribute data points including not only numerical attributes but also category attributes and mixed attributes, the fuzzy clustering algorithm is combined with attribute weighted optimization, and then, the optimization is derived. Iterative formula and form show a weighted clustering algorithm to perform cluster analysis on relevant influencing factors in book data management. Digital library is not a library entity: it corresponds to various real social activities of public information management and dissemination and is manifested in various new information resource organizations and information dissemination services. As a fuzzy association rule mining algorithm, FP-growth algorithm is obviously better than Apriori algorithm in execution efficiency. However, due to the lack of fuzzy attributes and the large space complexity, the FP-growth algorithm cannot achieve effective multilayer association rule mining when dealing with large transaction databases, such as library databases. First, the algorithm divides the large-scale book transaction database into several sub-databases according to the transaction of the first item and constructs the corresponding sub-FP-tree structure; then, the parent items that are not frequent items in the hierarchical tree are filtered out in real time to reduce the scanning space.

A Fuzzy Association Rules Mining Algorithm with Fuzzy Partitioning Optimization for Intelligent Decision Systems

A Fuzzy Association Rules Mining Algorithm with Fuzzy Partitioning Optimization for Intelligent Decision Systems

Smart solar powered hydroponics system using internet of things and fuzzy association rule mining

Hydroponics is an alternative to limited space agriculture by involving electrical energy for irrigating nutrients for plants. Apart from using an AC power source, hydroponics can also use a DC power source which makes it more portable and can be placed in a location far from the PLN(State Electricity Company) electricity source. DC power sources also have the potential to be supplied by renewable energy sources such as solar power directly without AC / DC conversion. However, solar energy sources have a weakness in the influence of the weather. The study proposes the implementation of the Internet of Things (IoT) to classify the condition of energy sources in its implementation of solar energy powered hydroponics system. The research involves the development of IoT devices to collect energy data generated by solar cells and stored in batteries and environmental conditions, namely light intensity, temperature, and humidity. The proposed IoT system classifies conditions based on the remaining energy in the battery and current weather conditions using the Fuzzy Association Rule Mining (FARM) algorithm. The system also considers the remaining hours of the day, because at night solar energy cannot be produced. The simulation result shows the developed solar-powered hydroponics system capable to keep supplying energy for 24 hours with three 10wp solar cells and a 7Ah battery for the deep flow technique hydroponics with the 24 pot with 1-meter stand and 20watt water pumps. The IoT system capable to collect data and the proposed regression algorithm FARM is capable to do the regression with low error rates (0.06 mean absolute error and 0.24 mean square error) with three days of data. The FARM also capable to extract environmental data that affect the energy generation and usage with a high average of supports (0.29) and confidence (0.32).

Research on Information Security Risk Assessment Method Based on Fuzzy Rule Set

With the increasing complexity of the network structure and the increasing size of the network, various network security incidents pose an increasing threat to the security of computer systems and the network. Especially, in the network environment, the diversified intrusion methods and application environment make the security of the network more fragile. In order to improve information security, based on fuzzy rule sets, this paper proposes a fuzzy association rule mining algorithm based on fuzzy matrix and applies it to security event correlation. In addition, this paper combines the embedded system to construct an information security risk assessment system and sets the system performance based on the actual situation. Finally, this paper carries out experimental design to verify the performance of the system and analyzes the experimental results by mathematical statistics. From the experimental research, it can be seen that the system constructed in this paper has a certain effect.

Exploration of Enhanced Fuzzy Association Rule Mining Algorithm

Exploration of Enhanced Fuzzy Association Rule Mining Algorithm

Application of data mining technology in alarm analysis of communication network

Nowadays, with the continuous development of science and technology, the social demand for the network is more and more, and with the continuous expansion of the network scale, the complexity of the network is increasing day by day. How to locate the fault accurately and quickly from a large number of alarm information has become a problem. To solve this problem, we must use computer technology to study the automatic analysis technology of alarm correlation. Therefore, this paper applies data mining technology to communication network alarm analysis, and based on fuzzy theory, it can accurately describe the relationship between network alarm and fault reason, and combines data mining technology with fuzzy theory to form the network alarm correlation analysis method of fuzzy association rule mining. On this basis, in order to mine the fuzzy association rules of the fuzzy alarm database directly and avoid the interference in the process of converting the alarm database to the transaction database, this paper proposes a dynamic time window fuzzy association rule mining algorithm. Through the simulation analysis, compared with the traditional data mining technology, the fuzzy association rules mining method combined with the fuzzy theory has better performance, and the further proposed dynamic time window fuzzy association rules mining algorithm greatly improves the accuracy of the association rules confidence, and has good application performance in the network alarm correlation analysis.

Cluster-Based Membership Function Acquisition Approaches for Mining Fuzzy Temporal Association Rules

In real-world applications, transactions are typically represented by quantitative data. Thus, fuzzy association rule mining algorithms have been proposed to handle these quantitative transactions. In addition, items generally have certain lifespans or temporal periods in which they exist in a database. Therefore, fuzzy temporal association rule mining algorithms have also been proposed in the literature. A key factor in the acquisition of fuzzy temporal association rules (FTARs) is the design of appropriate membership functions. Because current approaches have been designed to generate membership functions for mining fuzzy association rules (FARs) in market-basket analysis, in this paper, we propose a membership function tuning mechanism for a fuzzy temporal association rule mining algorithm. The proposed approach modifies an existing cluster-based method to generate unique membership functions that are specifically tailored to each item in a dataset. Two factors are utilized to decide the appropriate membership functions of each item: (1) the density similarity among intervals corresponding to the density similarity within intervals, and (2) the information closeness within an interval corresponding to the similarity in the number of data points between intervals. A parameter θ is used to indicate the relative importance of these two factors. As a result, the membership functions are generated based on the quantitative ranges of individual items, and the generated membership functions of items are different in terms of the values of each interval and the number of intervals. The generated membership functions are subsequently used in a fuzzy temporal association rule mining algorithm. Computational experiments were conducted on both a synthetic dataset and a real-world one to demonstrate the effectiveness of the proposed approach.

Incremental Fuzzy Association Rule Mining for Classification and Regression

The aim of mining fuzzy association rules is to find both the association and the casual relationships between the itemsets. With the arrival of dynamic data, the fuzzy association rules should be updated in real time. However, most of the existing algorithms must remine the updated database and can only be applied in classification. This paper proposes an incremental fuzzy association rule mining algorithm to solve classification and regression problems. First, the sliding window is adopted to divide the fuzzy dataset. Second, the dynamic fuzzy variable selection algorithm is adopted to select variables for reducing the search space of the fuzzy association rule mining. Finally, in each sliding window, the result of variable selection is used to incrementally mine the causal fuzzy association rules with the fuzzy Eclat algorithm. When new data are added, the process judges whether concept drift occurs, and if so, the rule set is updated; otherwise, the original rule set is still applied. The weights of the rules are calculated to find the evolving relationship. The simulation result shows that this algorithm can improve accuracy and efficiency.

Performance Analysis of Enhanced Fuzzy Association Rule Mining Algorithm with Levenstein Distance Using Contact Lens Dataset

Association Rule Mining (ARM) with fuzzy logic perception smooth the progress of the easy process of mining of underlying frequent or recurrent patterns based on their own frequencies in the form of association rules from any transactional and relational datasets containing items to signify the most recent trends in the given dataset. These mined recurrent patterns or fuzzy association rules employ either for physical data analysis or also influenced to compel further mining tasks like categorization (classification) and collecting (clustering) which helps domain area experts to automate decision-making. In the concept of data mining, generally fuzzy Association Rule Mining (FARM) technique has been comprehensively adopted in transactional and relational datasets those datasets containing items who has a fewer to medium amount of attributes/dimensions. Few techniques have also adopted for high dimensional dataset also, but whether those techniques have also work for low dimensional datasets are yet to be proven out. Hence, in this paper we propose E-FAR-HD algorithm which is an enhanced version of FAR-HD algorithm that designed exclusively for large or high-dimensional datasets. We have designed this EFAR-HD algorithm that increases the accuracy of FAR-HD algorithm on the smaller datasets and remove the chances of misses when FAR-HD has tested on smaller datasets such as contact lens or patient dataset.

Mining generalized positive and negative inter-cross fuzzy multiple-level coherent rules

Machine-learning and data-mining techniques have been developed to turn data into useful task-oriented knowledge. The algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level or multiple levels. Mining association s among itemsets only by using support and confidence thresholds at different levels of hierarchical data would not give interesting rules both for binary or quantitative data. This paper proposes a two phase algorithm that mines rare generalized fuzzy coherent rules at inter-cross level hierarchies. During phase-I both positive and negative fuzzy coherent rules are mined and in Phase-II, rare generalized fuzzy coherent rules are extracted from the resultant rules obtained from Phase-I. The algorithm framework works on top down methodology in generating positive and negative fuzzy coherent rules and mining rare generalized rules from it. Experiments conducted using synthetic dataset show the performance of the proposed algorithm in terms of the number of rare generalized rules generated, compared to fuzzy multiple-level association rule mining algorithm.

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.

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 %.

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.

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.

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.

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.

Weighted Association Rules Mining Algorithm Research

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

An Improvement of Fuzzy Association Rules Mining Algorithm Based on Redundancy of Rules

In data mining approach, quantitative attributes should be appropriately dealt with as well as Boolean attributes. This paper presents an essential improvement for extracting fuzzy association rules from a database. The objective of this paper is to improve the computational time of mining and to prune extracted redundant rules simultaneously for an actual data mining application. In this paper, we define the redundancy of fuzzy association rules as a new concept for mining and prove essential theorems concerning the redundancy of fuzzy association rules. Then, we propose a basic algorithm based on the Apriori algorithm for rule extraction utilizing the redundancy of the extracted rules. The essential performance of the algorithmis evaluated through numerical experiments using benchmark data. Fromthe results, themethod is found to be promising in terms of computational time and redundant-rule pruning.

Integrated Rule Mining Based on Fuzzy GNP and Probabilistic Classification for Intrusion Detection

With the increasing popularity of the Internet, network security has become a serious problem recently. How to detect intrusions effectively becomes an important component in network security. Therefore, a variety of algorithms have been devoted to this challenge. Genetic network programming is a newly developed evolutionary algorithm with directed graph gene structures, and it has been applied to data mining for intrusion detection systems providing good performances in intrusion detection. In this paper, an integrated rule mining algorithm based on fuzzy GNP and probabilistic classification is proposed. The integrated rule mining uses fuzzy class association rule mining algorithm to extract rules with different classes. Actually, it can deal with both discrete and continuous attributes in network connection data. Then, the classification is done probabilistically using different class rules. The integrated method showed excellent results by simulation experiments.

A Descriptive Framework for the Multidimensional Medical Data Mining and Representation

Problem statement: Association rule mining with fuzzy logic was explored by research for effective datamining and classification. Approach: It was used to find all the rules existing in the transactional database that satisfy some minimum support and minimum confidence constraints. Results: In this study, we propose new rule mining technique using fuzzy logic for mining medical data in order to understand and better serve the needs of Multidimensional Breast cancer Data applications. Conclusion: The main objective of multidimensional Medical data mining is to provide the end user with more useful and interesting patterns. Therefore, the main contribution of this study is the proposed and implementation of fuzzy temporal association rule mining algorithm to classify and detect breast cancer from the dataset.