Extraction Of Association Rules Research Articles

Dynamic Graph based Method for Mining Text Data

An improved graph based association rules mining (ARM) approach to extract association rules fromtext databases is proposed in this paper. The text document in the proposed technique is read only once to lookfor the terms whose occurrences are greater than some threshold value, these terms are stored in a file with theirfrequencies, then they are represented as nodes in a weighted directed graph where edges represent relationsbetween these terms, the edges will denote the associations between terms while the edges' weights denote thestrength or confidence of these rules. The proposed method is called Dynamic Graph based Rule Mining fromText (DGRMT) because the graph is built level by level according the length of a sentence (number of frequentterms). Weighted subgraph mining is used to ensure the efficiency and throughput of the proposed technique;only the most frequent subgraphs are extracted. The proposed technique is validated and evaluated using realworld textual data sets and compared with one of the best graph based rule mining technique, which is algorithmfor Generating Association Rules based on Weighting scheme(GARW). The results determine that the proposed approach is better than GARW on almost all textual datasets.

서울을 방문한 외국인 개별자유여행객(FIT)의 다관광지 방문패턴: 데이터마이닝을 활용한 첫방문객과 재방문객 비교

In the study of multi-destination travel patterns, the spatial associations of tourist sites may vary between tourist groups (i.e., first visitors vs. repeat visitors). In this study, FIT tourists" multi-destination travel patterns are explored using data mining methods. The purposes of this study is (1) to extract association rules of multi-destination travel patterns using inbound FIT tourists statistics in Korea, and (2) to compare the association rules between first visitors vs. repeat visitors. Association rule mining was ran on R3.6.3 program, and three parameters (i.e., support, confidence, and lift) were utilized. Results showed that there are 30 rules for the first visitor group whereas there are 9 rules for the repeat visitor groups. This implies that revisit tourists could be more focused and simple in their multi-destination travel patterns when visiting multiple tourist attractions in a city, compared to their counterparts. It is expected that this research will be used to develop tourist recommendation systems, provide tourist information efficiently, and establish a marketing strategy in conjunction with tourist associated destinations.

Closed Frequent Itemsets Mining Based on It-Tree

In the last decade, the amount of collected data, in various computer science applications, has grown considerably. These large volumes of data need to be analysed in order to extract useful hidden knowledge. This work focuses on association rule extraction. This technique is one of the most popular in data mining. Nevertheless, the number of extracted association rules is often very high, and many of them are redundant. In this paper, we propose an algorithm, for mining closed itemsets, with the construction of an it-tree. This algorithm is compared with the DCI (direct counting & intersect) algorithm based on min support and computing time. CHARM is not memery-efficient. It needs to store all closed itemsets in the memory. The lower min-sup is, the more frequent closed itemsets there are so that the amounts of memory used by CHARM are increasing.

Research on the inheritance and protection of data mining technology in national sports

SummaryIn order to improve the ability of inheritance and protection of national sports, the association data mining model of inheritance and protection of national sports is proposed based on feature extraction of association rules. In this article, a new neural fuzzy normal form system is designed, which can perform both feature analysis and SI(sine) integration. The time series analysis model of the inheritance and protection correlation data in the national sports are constructed, the overall frame model of the inheritance and protection correlation data mining in the national sports is designed. The ability of adaptive data fusion and feature registration is improved. The simulation results show that the proposed method has good accuracy and strong ability of feature matching in the correlation data mining of inheritance and protection in national sports, and it can improve the function of inheritance and protection of national sports.

Intelligent mining algorithm for complex medical data based on deep learning

In order to address the problems of low precision, long time-consuming and low recall rate in mining complex attribute medical data in medical information, an intelligent mining algorithm for complex attribute medical data based on deep learning is proposed. Discretized medical data with complex attributes and converted it into a data type suitable for deep learning research, the convolutional neural network is used to analyze the association mapping relationship between complex attribute medical data sets and extract association rules of data. According to the degree of association between complex attribute medical data sets in multi-dimensional subspace to realize the effective mining of complex attribute medical data. The results show that the proposed algorithm takes less time and can extract association rules accurately, the data priority control efficiency is higher, the data mining accuracy is better, and the data mining recall rate is much higher than other methods, which verifies the feasibility of the proposed algorithm.

New Descriptors of Textual Records: Getting Help from Frequent Itemsets

The analysis of numerical data, whether structured, semi-structured, or raw, is of paramount importance in many sectors of economic, scientific, or simply social activity. The process of extraction of association rules is based on the lexical quality of the text and on the minimum support set by the user. In this paper, we implemented a platform named “IDETEX” capable of extracting itemsets from textual data and using it for the experimentation in different types of clustering methods, such as [Formula: see text]-Medoids and Hierarchical clustering. The experiments conducted demonstrate the potential of the proposed approach for defining similarity between segments.

Rough Kansei Mining Model for Market-Oriented Product Design

Companies need to develop new products towards customer's satisfaction in order to survive in the boom and bust cycle in todays’ economy. The capturing of customer satisfaction depends on customer needs, and generally, understanding emotions has a challenge for designers. Kansei engineering is a type of methodology to help customers and designers analyze needs and emotion for the new product development. Producing new product design with Kansei data increases customer satisfaction and helps to reach market goals. In this study, a market-oriented baby cradle design methodology is proposed, and we obtain the new product strategies with association rule extraction by using rough set theory. To obtain efficient rules, beforehand we selected sales knowledge-related Kansei words with our proposed approach: cost-based and multiclass decision-theoretic rough set (DTRS) attribute reduction. The new product design strategies which are obtained with proposed design methodology are consistent with customer expectations (mood space) and expert opinions (design team).

RETRACTED ARTICLE: The construction of hierarchical network model and wireless activation diffusion optimization model in English teaching

Wireless communication plays an important role in modern higher education. This paper first analyzes the storage mechanism and data structure of the hierarchical network model, then fits the time series of user behavior attribute data, and uses the information-filtering algorithm to filter the interference information and redundant information in the social network. The feature extraction of association rules applies fuzzy data clustering to the mining and clustering of relational data in hierarchical networks. The simulation results show that the algorithm model has high accuracy and reliability and improves the ability of deep mining in English teaching.

TEXT MINING MENGGUNAKAN GENERATE ASSOCIATION RULE WITH WEIGHT (GARW) ALGORITHM UNTUK ANALISIS TEKS WEB CRAWLER

Text mining is widely used to find hidden patterns and information in a large number of semi and unstructured texts. Text mining extracts interesting patterns to explore knowledge from textual data sources. Association rule extraction GARW (Generating Association Rule using Weighting Scheme) can be used to find knowledge from a collection of web content without having to read all the web content manually from the many search results of crawlers. The GARW algorithm is a development of a priori to produce relevant association rules. From the results of this knowledge discovery can facilitate netizens users in finding relevant information from search keywords without having to review one by one web content generated from search engine searches.

TEXT MINING MENGGUNAKAN GENERATE ASSOCIATION RULE WITH WEIGHT (GARW) ALGORITHM UNTUK ANALISIS TEKS WEB CRAWLER

Text mining is widely used to find hidden patterns and information in a large number of semi and unstructured texts. Text mining extracts interesting patterns to explore knowledge from textual data sources. Association rule extraction GARW (Generating Association Rule using Weighting Scheme) can be used to find knowledge from a collection of web content without having to read all the web content manually from the many search results of crawlers. The GARW algorithm is a development of a priori to produce relevant association rules. From the results of this knowledge discovery can facilitate netizens users in finding relevant information from search keywords without having to review one by one web content generated from search engine searches.

Sensing the Web for Induction of Association Rules and their Composition through Ensemble Techniques

Abstract Starting from geophysical data collected from heterogeneous sources, such as meteorological stations and information gathered from the web, we seek unknown connections between the sampled values through the extraction of association rules. These rules imply the co-occurrence of two or more symbols in the same representation, and the rule confidence may vary according to the collected data. We propose, starting from traditional algorithms such as FP-Growth and Apriori, the creation of complex association rules through boosting of simpler ones. The composition enables the creation of rules that are robust and let emerge a larger number of interesting rules.

Analysis of Cyberbullying Incidence among Filipina Victims: A Pattern Recognition using Association Rule Extraction

Analysis of Cyberbullying Incidence among Filipina Victims: A Pattern Recognition using Association Rule Extraction

Research on Resource Allocation Optimization of Information Management System Based on Big data Association Mining

Aiming at the problem that the resource allocation accuracy of information management system in cloud computing environment is not high, the resource allocation method of information management system in cloud computing environment is improved and designed. A resource allocation algorithm of information management system is proposed based on big data association mining. The information of resource distribution in information management system is fused and reconstructed according to the key words and semantic association features, and then the feature extraction of association rules of information management system resources in cloud computing environment is carried out. The extracted association rules are used for big data training set to allocate the resources of information management system, and big data association mining and adaptive scheduling method are used for adaptive optimization control of resource allocation. The simulation results show that this method can improve the resource allocation accuracy of information management system, the anti-interference ability of resource allocation, and the efficiency of resource utilization and information management.

Türk Perakende Şirketindeki Çevirimiçi Alış Verişler için İlişkililik Kurallarını Çıkarılması: Durum Çalışması

The extracting association rules of inter-user-product relations used by companies in decision-making processes have been popular for some time, especially for market basket analysis. In this study it is aimed to discover association rules from original online store transaction of a Turkish retail company, in order to help administrator and decision maker also Customer Relationship Management department to initiate campaigns. The main objective is to find out which product item sets are bought together. In order to better compare the results the data are analyzed with and without clustering according to range of ages and gender. Data mining Association analysis methods such as Apriori Algorithm, FP-Growth (Frequent Pattern) then applied which are used to extract association rules. Moreover some of the collaborative filtering metrics namely Jaccard, Pearson, and Cosine function are used to understand the association between products to obtain a recommendation system. The proposed recommendation methods successfully recommended the associated product for the obtained original dataset as high as %65 accuracy. Obtained association rules are shared with the marketing department to initiate and direct forthcoming marketing campaigns.

An operational parameter optimization method based on association rules mining for chiller plant

The traditional mechanism models of a chiller plant of HVAC are complicated with multiple variables and many constraints, so that it’s burdensome to optimize those operational parameters. Moreover, the optimization methods based on mechanism models are unpractical to be applied in the engineering projects. Therefore, an operational parameter optimization method based on the unsupervised data mining technology is proposed in this paper and verified with a large number of field operational data of the chiller plant of a shopping mall in the subtropical area. The unsupervised data mining procedure is illustrated in detail, including data preparation, data partitioning, strong association rules extraction by Apriori algorithm and so on. The definition, selection and discretization methods of external and operational parameters are described to determine the mining target and divide typical operating conditions. At last, 54 and 70 strong association rules, respectively, for the single larger chiller operating mode and the single smaller one under typical operating conditions are extracted. Simulation results shows that the energy consumption of the chiller plant is reduced by 11.60% and 13.33% after optimization, respectively, on the studied days in summer and that in winter. The analysis results mean that after optimization, the energy performance of a chiller plant was significantly improved. The strong association rules are easily utilized in the engineering projects, in terms of their simplicity and feasibility. And this method can also be used in other fields when there are enough effective operational data.

A Fuzzy Approach to Determining Critical Factors of Diabetic Retinopathy and Enhancing Data Classification Accuracy

Diabetic retinopathy (DR) is a chronic and progressive sight-threatening complication of retinal microvasculature associated with diabetes mellitus. These morphological subtle variations in the retina can result in visual impairment or eventual vision loss if not treated in time. The early detection and diagnosis of DR is mandatory to safeguard the patient’s vision. To investigate and intervene at an earlier stage, we aim to systematically extract and analyze different DR factors, such as retinal condition of eyes, presence of microaneurysms (MAs), MAs with exudate, diameter of optical disk (OD), and Euclidian distance between macula and center of the OD. Considering the DR attributes, first we find out the important attributes of DR by applying fuzzy analytical network process to rank attributes from the most to the least related factors to DR. Then, a transformed fuzzy neural network is created to enhance the classification accuracy. Finally, we extract association rules among the selected attributes of DR to reveal their importance and degree of severity. Findings of this study reveal a new perspective on the treatment of DR at the early stage and prevention from any complications to improve the quality of life of all people.

Association rule mining using chaotic gravitational search algorithm for discovering relations between manufacturing system capabilities and product features

An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for efficient and cost-effective product development and production. This article proposes a chaotic gravitational search algorithm–based association rule mining method for discovering the hidden relationship between manufacturing system capabilities and product features. The extracted rules would be utilized to predict capability requirements of various machines for the new product with different features. We use two strategies to incorporate chaos into gravitational search algorithm: one strategy is to embed chaotic map functions into the gravitational constant of gravitational search algorithm; the other is to use sequences generated by chaotic maps to substitute random numbers for different parameters of gravitational search algorithm. In order to improve the applicability of chaotic gravitational search algorithm–based association rule mining, a novel overlapping measure indication is further proposed to eliminate those unuseful rules. The proposed method is relatively simple and easy to implement. The rules generated by chaotic gravitational search algorithm–based association rule mining are accurate, interesting, and comprehensible to the user. The performance comparison indicates that chaotic gravitational search algorithm–based association rule mining outperforms other regular methods (e.g. Apriori) for association rule mining. The experimental results illustrate that chaotic gravitational search algorithm–based association rule mining is capable of discovering important association rules between manufacturing system capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

Identifying mutation positions in all segments of influenza genome enables better differentiation between pandemic and seasonal strains

Influenza has a negative sense, single-stranded, and segmented RNA. In the context of pandemic influenza research, most studies have focused on variations in the surface proteins (Hemagglutinin and Neuraminidase). However, new findings suggest that all internal and external proteins of influenza viruses can contribute in pandemic emergence, pathogenicity and increasing host range. The occurrence of the 2009 influenza pandemic and the availability of many external and internal segments of pandemic and non-pandemic sequences offer a unique opportunity to evaluate the performance of machine learning models in discrimination of pandemic from seasonal sequences using mutation positions in all segments. In this study, we hypothesized that identifying mutation positions in all segments (proteins) encoded by the influenza genome would enable pandemic and seasonal strains to be more reliably distinguished. In a large scale study, we applied a range of data mining techniques to all segments of influenza for rule discovery and discrimination of pandemic from seasonal strains. CBA (classification based on association rule mining), Ripper and Decision tree algorithms were utilized to extract association rules among mutations. CBA outperformed the other models. Our approach could discriminate pandemic sequences from seasonal ones with more than 95% accuracy for PA and NP, 99.33% accuracy for NA and 100% accuracy, precision, specificity and sensitivity (recall) for M1, M2, PB1, NS1, and NS2. The values of precision, specificity, and sensitivity were more than 90% for other segments except PB2. If sequences of all segments of one strain were available, the accuracy of discrimination of pandemic strains was 100%. General rules extracted by rule base classification approaches, such as M1-V147I, NP-N334H, NS1-V112I, and PB1-L364I, were able to detect pandemic sequences with high accuracy. We observed that mutations on internal proteins of influenza can contribute in distinguishing the pandemic viruses, similar to the external ones.

Using Localized Features for Analyzing College Students’ Imagination

The analysis of imagination has become popular in recent years because imagination is one of the key components of creativity and innovation. For extracting students’ implicit degrees and thought processes of imagination, we use frequent pattern mining and association rule extraction to localize the features and explain the deep meanings of imagination in the study. By our observations, these two methods may sometimes explore meaningless frequent patterns and rules on a global sparse dataset. In order to eliminate such phenomena when mining with these two methods, we use a localized feature approach called forecast with clustering and integration (FCI) to improve the drawbacks of two methods on a sparse dataset. The approach consists of two strategies. One is clustering and the other is the prediction based on integration from (1) frequent patterns, (2) association rule pruning with correlation, and (3) forecast with linear regression. The former strategy can reduce the number of samples and highlight the features of imagination and the latter strategy can prune meaningless information and predict the trend of scores from imagination input data. Experimental results show both proposed approaches can localize special features, thereby providing supervisors with meaningful information about students’ degrees and thought processes of imagination.

Extraction of association rules in the schematic design of product service system based on Pareto-MODGDFA

Product service system (PSS) forces the manufacturing enterprise to shift from simple product design to an overall solution design for products and services. Schematic design involves the combination of product modules and service modules. The existing rule mining methods often experience difficulties when extracting association configuration rules in the high-dimensional space of the product service system configuration. In this paper, we propose a new method to mine association rules by integrating a multi-dimension and multi-objective double group discrete Firefly Algorithm (MODGDFA) with Pareto rules. The association rule of schematic design is mainly extracted from a large number of historical design databases by using MODGDFA. The MODGDFA uses fitness function, support degree, confidence degree, interest degree, and comprehensibility to evaluate the quality of association rules. The Pareto-MODGDFA model is constructed to establish a relevant model of the PSS. In this method, discrete Firefly Algorithm coding is adopted. Two subgroups are updated according to their respective position updating method. The optimal fireflies of the two sub-groups exchange information to obtain different sets of non-dominated solutions and to extract the potential association rules from the database. The Pareto principle in the evolution process is used to evaluate the merits and demerits of the firefly in the multi-objective functions and reduce redundant rules. The effectiveness and feasibility of the proposed method are demonstrated by using CNC machine tools of the product service system schematic design.