Fuzzy Data Mining Algorithm Research Articles

The Role of Mining and Detection of Big Data Processing Techniques in Cybersecurity

Abstract The need for advanced detection methods has become more critical in light of the increasing prevalence of network security incidents. This study proposes a novel approach to network security detection using a fuzzy data mining algorithm, addressing the rising challenges in big data processing and network security. The paper outlines the evolution of big data analytics by exploring the integration of network security detection, data mining, and structural feature analysis. Data for this research was collected using a sniffer device and underwent extensive preprocessing to ensure diversity and applicability. To overcome the limitations of traditional data mining, such as the issue of sharp boundaries, this method combines fuzzy logic with data mining techniques, enhancing conventional network security protocols. Simulation experiments demonstrate the efficacy of this fuzzy mining-based approach, with results showing 987,238 predicted positive cases, 93,951 of which were accurate. The method achieves an impressive 93.65% accuracy and 92.55% recall rate, proving its capability to promptly identify and mitigate suspicious network activities.

Performance Evaluation of Tourism Human Resource Management Based on Fuzzy Data Mining

Tourism human resources performance management is an important means of enterprise core competitiveness; the assessment results is good or bad will directly affect the staff's work enthusiasm and attitude; the use of tourism human resources, configuration, and performance carries on the scientific, reasonable, and effective comprehensive evaluation and can fully stimulate the potential of employees. To continuously improve the work efficiency, quality, and level of staff, which has certain application value and practical significance for the long‐term and healthy development of tourism industry, this article analyzes the current situation of tourism human resource performance management, introduces the data mining technology and fuzzy data mining technology, and then gives the fuzzy data mining algorithm and detailed fuzzy data mining steps, through the fuzzy data mining technology to evaluate the performance of tourism human resource management. It also analyzes the key factors and existing problems of human resource management in tourism industry and puts forward innovative thinking countermeasures for the performance management of human resources in the tourism industry.

Construction of Social Security Fund Cloud Audit Platform Based on Fuzzy Data Mining Algorithm

Guided by the theories of system theory, synergetic theory, and other disciplines and based on fuzzy data mining algorithm, this article constructs a three-tier social security fund cloud audit platform. Firstly, the article systematically expounds the current situation of social security fund and social security fund audit, such as the technical basis of cloud computing and data mining. Combined with the actual work, the necessity and feasibility of building a cloud audit platform for social security funds are analyzed. This article focuses on the construction of the cloud audit platform for social security funds. The general idea of using fuzzy data mining algorithm to build the social security fund audit cloud platform is to compress the knowledge contained in a large number of data into the weights between nodes and optimize the weights through the learning of the neural network system. Through the optimization function, the information contained in the neural network is stored in a few weights as far as possible. The main information is further highlighted by network clipping and removing weights that have little impact on the output.

Security Protection Technology in Multi-Attribute Data Transmission Based on Fuzzy Genetic Algorithm

Because the traditional data transmission security protection methods ignore the process of multi-attribute data detection, resulting in the abnormal data false alarm rate, high missing alarm rate, eliminating accuracy and other problems, a multi-attribute data transmission security protection method based on fuzzy genetic algorithm is proposed. The anomaly detection method based on fuzzy data mining and genetic algorithm is adopted to detect and obtain the abnormal data in the multi-attribute data transmission, and the abnormal data in the multi-attribute data transmission is eliminated through the abnormal data elimination method based on PSO and SVM, so as to realize the security protection of multi-attribute data transmission. It is verified that the recall rate and accuracy rate of abnormal data of the proposed method are higher than 95%, and the removal accuracy of abnormal data is higher. Moreover, this method is far better than the comparison method in positive likelihood ratio and Jordan index, and has higher application value.

Model Data Mining dan Rasio Altman untuk Prediksi Financial Distress Perusahaan

Financial distress is the stage or situation of a decline in the company's financial condition before a business failure occurs. This failure is related to liquidation, where the company fails to run its operations for profit. Financial distress prediction methods have been long developed in the field of finance using Multiple Discriminant Analyst (MDA). The most widely used MDA method is the Altman model ratio. This method is used as an early warning to the company's financial condition. This study aims to analyze the neuro fuzzy data mining algorithm with input data from the financial ratio of the Altman model to predict the company's financial distress. Research data are from the Indonesia Stock Exchange (IDX) website at www.idx.co.id. Data population between 2012-2017 LQ45 issuers are 80 companies. Preliminary data from the company's financial statements are calculated by Altman's ratio to get zscore values ??of three types of categories. These values ??are arranged in time series of four backward periods which will be used as input data for the neuro fuzzy algorithm ANFIS model. The next step is the formation of algorithms, training, testing, simulation and model evaluation using the GUI application from the Matlab program. The results of the model are predictive values ??that are compared with the results of the Altman ratio calculation of predicted years. The final results of the study show the predicted value of the ANFIS model of the Triangle membership function looks very optimal, consistent, and efficient. It was proven that the average predicted value of 9.95% was close to the actual value of the calculated year, while the results of the ANFIS model of the Trapezoid, Gauss and G-bell membership functions seemed less than optimal.
 Keywords: Financial Distress, Neuro Fuzzy, Prediction, Altman Ratio.

High risk intrusion data mining algorithm based on large database

The current method of data mining has poor mining accuracy. Therefore, this paper proposes a fuzzy clustering data mining algorithm of SFLA based on selection and mutation mechanism. The algorithm firstly collects the intrusion data and extracts the features. Then the fuzzy clustering SFLA algorithm is used to complete the research of data mining algorithm in high risk intrusion. Experimental results show that the proposed algorithm improves the accuracy of data mining

Root-Cause and Defect Analysis based on a Fuzzy Data Mining Algorithm

Manufacturing organizations have to improve the quality of their products regularly to survive in today’s competitive production environment. This paper presents a method for identification of unknown patterns between the manufacturing process parameters and the defects of the output products and also of the relationships between the defects. Discovery of these patterns helps practitioners to achieve two main goals: first, identification of the process parameters that can be used for controlling and reducing the defects of the output products and second, identification of the defects that very probably have common roots. In this paper, a fuzzy data mining algorithm is used for discovery of the fuzzy association rules for weighted quantitative data. The application of the association rule algorithm developed in this paper is illustrated based on a net making process at a netting plant. After implementation of the proposed method, a significant reduction was observed in the number of defects in the produced nets.

Understanding Open Source Software Evolution Using Fuzzy Data Mining Algorithm for Time Series Data

Source code management systems (such as Concurrent Versions System (CVS), Subversion, and git) record changes to code repositories of open source software projects. This study explores a fuzzy data mining algorithm for time series data to generate the association rules for evaluating the existing trend and regularity in the evolution of open source software project. The idea to choose fuzzy data mining algorithm for time series data is due to the stochastic nature of the open source software development process. Commit activity of an open source project indicates the activeness of its development community. An active development community is a strong contributor to the success of an open source project. Therefore commit activity analysis along with the trend and regularity analysis for commit activity of open source software project acts as an important indicator to the project managers and analyst regarding the evolutionary prospects of the project in the future.

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 a Complete Set of Fuzzy Multiple-Level Rules

Most conventional data-mining algorithms identify relationships among transactions using binary values and find rules at a single-concept level. Transactions with quantitative values and items with hierarchical relationships are, however, commonly seen in real-world applications. In the past, we proposed a fuzzy multiple-level mining algorithm for extracting knowledge implicit in transactions stored as quantitative values. In that algorithm, each attribute uses only the linguistic term with the maximum cardinality in the mining process. The number of linguistic items is thus the same as that of the original attributes in the transaction database. This constraint allows the processing time as efficient as mining of non-fuzzy association rules. However, the fuzzy association rules derived in that way are not complete, as some possible fuzzy association rules might be missing. This paper proposes a new fuzzy data-mining algorithm for extracting all possible fuzzy association rules from transactions stored as quantitative values. The proposed algorithm can derive a more complete set of rules but with more computation time than the previous method. Trade-off thus exists between the computation time and the completeness of rules. Choosing an appropriate mining method thus depends on the requirement of the application domains.

Distributed and Scalable Intrusion Detection System Based on Agents and Intelligent Techniques

The Internet explosion and the increase in crucial web applications such as ebanking and e-commerce, make essential the need for network security tools. One of such tools is an Intrusion detection system which can be classified based on detection approachs as being signature-based or anomaly-based. Even though intrusion detection systems are well defined, their cooperation with each other to detect attacks needs to be addressed. Consequently, a new architecture that allows them to cooperate in detecting attacks is proposed. The architecture uses Software Agents to provide scalability and distributability. It works in two modes: learning and detection. During learning mode, it generates a profile for each individual system using a fuzzy data mining algorithm. During detection mode, each system uses the FuzzyJess to match network traffic against its profile. The architecture was tested against a standard data set produced by MIT's Lincoln Laboratory and the primary results show its efficiency and capability to detect attacks. Finally, two new methods, the memory-window and memoryless-window, were developed for extracting useful parameters from raw packets. The parameters are used as detection metrics.

Discovering fuzzy inter- and intra-object associations

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Recently, the fuzzy and the object concepts have been very popular and used in a variety of applications, especially for complex data description. This paper thus proposes a new fuzzy data-mining algorithm for extracting interesting knowledge from quantitative transactions stored as object data. Each item itself is thought of as a class, and each item purchased in a transaction is thought of as an instance. Instances with the same class (item name) may have different quantitative attribute values since they may appear in different transactions. The proposed fuzzy algorithm can be divided into two main phases. The first phase is called the fuzzy intra-object mining phase, in which the linguistic large itemsets associated with the same classes (items) but with different attributes are derived. Each linguistic large itemset found in this phase is thought of as a composite item used in phase 2. The second phase is called the fuzzy inter-object mining phase, in which the large itemsets are derived and used to represent the relationship among different kinds of objects. An example is used to illustrate the algorithm. Experimental results are also given to show the effects of the proposed algorithm.

Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms

Different studies have proposed methods for mining fuzzy association rules from quantitative data, where the membership functions were assumed to be known in advance. However, it is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for mining fuzzy association rules. This paper thus presents a new fuzzy data-mining algorithm for extracting both fuzzy association rules and membership functions by means of a genetic learning of the membership functions and a basic method for mining fuzzy association rules. It is based on the 2-tuples linguistic representation model allowing us to adjust the context associated to the linguistic term membership functions. Experimental results show the effectiveness of the framework.

Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy

Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consist of quantitative values. This paper, thus, proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. A genetic algorithm (GA)-based framework for finding membership functions suitable for mining problems is proposed. The fitness of each set of membership functions is evaluated by the fuzzy-supports of the linguistic terms in the large 1-itemsets and by the suitability of the derived membership functions. The evaluation by the fuzzy supports of large 1-itemsets is much faster than that when considering all itemsets or interesting association rules. It can also help divide-and-conquer the derivation process of the membership functions for different items. The proposed GA framework, thus, maintains multiple populations, each for one item's membership functions. The final best sets of membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experiments are conducted to analyze different fitness functions and set different fitness functions and setting different supports and confidences. Experiments are also conducted to compare the proposed algorithm, the one with uniform fuzzy partition, and the existing one without divide-and-conquer, with results validating the performance of the proposed algorithm.

Fuzzy Data Mining and Genetic Algorithms Applied to Intrusion Detection

Fuzzy Data Mining and Genetic Algorithms Applied to Intrusion Detection

Mining Frequent Fuzzy Grids in Dynamic Databases with Weighted Transactions and Weighted Items

Incremental mining algorithms that derive the latest mining output by making use of previous mining results are attractive to business organisations. In this paper, a fuzzy data mining algorithm for incremental mining of frequent fuzzy grids from quantitative dynamic databases is proposed. It extends the traditional association rule problem by allowing a weight to be associated with each item in a transaction and with each transaction in a database to reflect the interest/intensity of items and transactions. It uses the information about fuzzy grids that are already mined from original database and avoids start-from-scratch process. In addition, we deal with "weights-of-significance" which are automatically regulated as the incremental databases are evolved and implant themselves in the original database. We maintain "hopeful fuzzy grids" and "frequent fuzzy grids" and our algorithm changes the status of the grids which have been discovered earlier so that they reflect the pattern drift in the updated quantitative databases. Our heuristic approach avoids maintaining many "hopeful fuzzy grids" at the initial level. The algorithm is illustrated with one numerical example and demonstration of experimental results are also incorporated.

A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. The fitness of each chromosome is evaluated by the number of large 1-itemsets generated from part of the previously proposed fuzzy mining algorithm and by the suitability of the membership functions. Experimental results also show the effectiveness of the framework.

A FUZZY DATA MINING ALGORITHM FOR INCREMENTAL MINING OF QUANTITATIVE SEQUENTIAL PATTERNS

In real world applications, the databases are constantly added with a large number of transactions and hence maintaining latest sequential patterns valid on the updated database is crucial. Existing data mining algorithms can incrementally mine the sequential patterns from databases with binary values. Temporal transactions with quantitative values are commonly seen in real world applications. In addition, several methods have been proposed for representing uncertain data in a database. In this paper, a fuzzy data mining algorithm for incremental mining of sequential patterns from quantitative databases is proposed. Proposed algorithm called IQSP algorithm uses the fuzzy grid notion to generate fuzzy sequential patterns validated on the updated database containing the transactions in the original database and in the incremental database. It uses the information about sequential patterns that are already mined from original database and avoids start-from-scratch process. Also, it minimizes the number of candidates to check as well as number of scans to original database by identifying the potential sequences in incremental database.

Mining fuzzy sequential patterns from quantitative transactions

Many researchers in database and machine learning fields are primarily interested in data mining because it offers opportunities to discover useful information and important relevant patterns in large databases. Most previous studies have shown how binary valued transaction data may be handled. Transaction data in real-world applications usually consist of quantitative values, so designing a sophisticated data-mining algorithm able to deal with various types of data presents a challenge to workers in this research field. In the past, we proposed a fuzzy data-mining algorithm to find association rules. Since sequential patterns are also very important for real-world applications, this paper thus focuses on finding fuzzy sequential patterns from quantitative data. A new mining algorithm is proposed, which integrates the fuzzy-set concepts and the AprioriAll algorithm. It first transforms quantitative values in transactions into linguistic terms, then filters them to find sequential patterns by modifying the AprioriAll mining algorithm. Each quantitative item uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of the original items. The patterns mined out thus exhibit the sequential quantitative regularity in databases and can be used to provide some suggestions to appropriate supervisors.

TRADE-OFF BETWEEN COMPUTATION TIME AND NUMBER OF RULES FOR FUZZY MINING FROM QUANTITATIVE DATA

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values. Transactions with quantitative values are however commonly seen in real-world applications. We proposed a fuzzy mining algorithm by which each attribute used only the linguistic term with the maximum cardinality int he mining process. The number of items was thus the same as that of the original attributes, making the processing time reduced. The fuzzy association rules derived in this way are not complete. This paper thus modifies it and proposes a new fuzzy data-mining algorithm for extrating interesting knowledge from transactions stored as quantitative values. The proposed algorithm can derive a more complete set of rules but with more computation time than the method proposed. Trade-off thus exists between the computation time and the completeness of rules. Choosing an appropriate learning method thus depends on the requirement of the application domains.