Data Mining Association Rules Research Articles

An Improved Distance Metric Clustering Algorithm for Association Rules

By mining association rules in large data, we can reveal useful information contained in the data and find out the relationship between things or the law of motion. However, because of the huge transaction data, the association rules obtained by mining are complex and massive. It is difficult to find useful association relations, especially when the re-demand is uncertain. To solve this problem, this paper first uses Apriori algorithm to mine association rules from a data set, then defines similarity measure between association rules, and applies DBSCAN clustering algorithm to association rules analysis. The analysis results show that this method is effective in association rules analysis.

Application of Signal Index for Association Evaluation

약물감시에서 약물부작용에 대한 자발적 신고체계는 판매된 약물에 대한 실마리정보를 확인하는 데 중요한 역할을 한다. 실마리정보는 인과관계의 정보가 없거나 입증자료가 불충분하여도 인과관계를 배제할 수 없어서 지속적인 관찰이 요구되는 정보라고 할 수 있다. 약물의 부작용에 대한 자발적 신고제도가 도입되고 한국의약품안전관리원이 출범하면서 자발적부작용신고시스템이 구축되었으며, 또한 신고가 된 자료들을 체계적으로 관리하게 되었다. 본 논문에서는 의약품 유해사례 분석을 위한 실마리정보 지표에 대해 데이터 마이닝에서의 연관성 규칙을 생성하기 위한 도구로의 활용 가능성을 타진하였다. 이를 위해 먼저 여러 형태의 신뢰도와 향상도와 같은 기본적인 연관성 측도와의 관계를 파악하였으며, 예제를 통하여 이들 통계량의 효율적인 측면을 탐색하였다. 그 결과, 실마리정보 지표들은 동시 발생 빈도가 증가함에 따라 모두 증가하는 것으로 나타난 반면에 불일치 빈도가 증가함에 따라서는 이들 모두가 감소하는 것으로 나타났다. 따라서 실마리정보 지표들은 모두 연관성 평가 측도로 바람직한 것으로 나타났으며, 이들 중에서는 부호를 가지는 정보 성분 측도나 Yule의 Q 측도가 바람직한 것으로 나타났다.The spontaneous reporting system for drug side effects in pharmacovigilance plays an important role in identifying signal on the drugs sold. The signal is information that requires continuous observation because there is no causal relationship information or the causal relationship can not be excluded even if the proof data is inadequate. A spontaneous reporting system for side effects of drugs was introduced. The Korea Pharmaceutical Safety Management Service (KSA) was established, and a spontaneous adverse reaction reporting system was established. In this paper, we have discussed the possibility of using the signal index for drug harmful case analysis as a tool to generate association rules in data mining. To do this, we first grasped the relationship between these statistics and the basic association measures such as various types of confidence and lift, and explored some efficient aspects of them through examples. As a result, the signal indicators were all increased as the coincidence frequency increased, but all of them decreased as the inconsistency frequency increased.

Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint

The basic idea of Apriori algorithm is first introduced in this paper, which is to find all frequent sets in a transaction. The frequent requirements of these frequent sets are greater than or equal to the minimum support of the set. On this basis, the working principle of the traditional Apriori algorithm is analyzed, and the existing problems are pointed out. To solve these problems, an improved Apriori algorithm is proposed for time series of frequent itemsets. Finally, on the basis of analyzing the methods and processes of mining association rules for time series, this improved time series Apriori algorithm for frequent itemsets is applied to mining association rules based on time constraints. The experimental results show that the improved Apriori algorithm is better than the traditional one in storage space.

Research on the Random Corresponding of Privacy Data Mining in the Association Rules of Cloud Computing

To strengthen data privacy protection, and improve data mining accuracy, random response mode is adopted to design privacy protection mining method based on association rules. Granular computing method and technology are applied to mining fields of association rules in data mining, and mining to association rules is researched in more extensive way from another perspective in this paper. Firstly, partial concealing mode is adopted to conceal and transform original privacy data and improve data security; secondly, associated frequent item set is utilized to construct simple and efficient privacy protection mining algorithm; finally, algorithm proposed is verified to have higher privacy and accuracy through theoretical analysis and experimental verification. After classical association rules mining algorithm is analyzed and researched in detail with its characteristics and restrictions summarized through examples in this paper, association rules mining model based on granular computing is proposed, which makes theoretical preparation for proposal and construction of association rules pick-up algorithm based on granular computing. Experimental result shows that association rules mining method based on granular computing is feasible and effective.

Penerapan Algoritma FP-Growth dalam Penentuan Pola Pembelian Konsumen pada Kain Tenun Medali Mas

In Kediri City there is a very popular woven fabric shop called Medali Mas. It has high sales transaction activity resulting in a large stack of data purchases. This data stack is examined as an information pattern for consumer purchases using data mining association rule techniques and FP-Growth algorithms. The FP-Growth algorithm uses the concept of development tree in searching for frequent item sets. The data used are, 26 types of woven fabric items and 200 transaction data provided that 2 or 3 types of items in 1 transaction. Determined minimum support value of 20 percent and minimum confidence value of 10 percent. It also used Chi-Square testing to find out how much correlation between variables from the results of frequent itemsets that have been calculated. The final result of the consumer purchasing pattern is obtained (m to no) when buying Semi sutra Lusi = grey, Pakan = Blue Flowers, then the consumer might buy Sarong Lusi = black, Pakan= green Lurik and Cotton Lusi= yellow, Pakan = Tosca Bamboo with the results of the correlation between variables at 19.1397274913.

A Case Study of Research Improvements in an Service Industry Upgrading the Knowledge Base of the Information System and the Process Management: Data Flow Automation, Association Rules and Data Mining

In this paper is analyzed a case study of an upgrade of an industry communication system developed by following ‘Frascati’ research guidelines. The goal of the proposed model is to enhance the industry knowledge Base –KB- by acting directly on information communication system improvements and data system integration, enabling automated process and data processing. The paper follow all the steps performed during the project development: the preliminary data infrastructure design, the information infrastructure improvements, and data processing. Data processing is performed by a calculus engine embedding data mining association rules and Artificial Neural Network –ANN- predictive algorithms thus improving the research. The calculus engine has been implemented by a multiple variables model where the contract data are preliminary processed in order to define functions classifying the operation processes and activating automatically the service process management. The business intelligence –BI- operations are performed mainly by the calculus engine optimizing industry performances. The goal of the paper is to show how research and development –R&D- can be applied by gaining and optimizing the knowledge and processes of an Italian industry working in car services. The project has been developed with the collaboration of the industry ACI Global working in roadside assistance services. By means of a research project resources, the information technology –IT- infrastructure has been improved by new solutions of the communication system and of the data transfer. The proposed case of study provides a model and a guideline to follow in order to apply research in industry acting directly on data and information network.

Efficient streaming data association rule mining

Recently, number of applications including social networks, stock market trading and sensor network devices generate a massive amount of data in the streaming form. Streaming data have characteristics different from static data, such as streaming data arrives continuously at high speed with huge amount. Mining and discovering information from these data is a non-trivial issue. Most of traditional algorithms have limitations to deal with streaming data, so there are new issues raised and need to be taken into consideration while developing techniques for mining association rules from such data. In this paper, a technique to mine an association rules from streaming data efficiently is proposed. The proposed technique develops a tree structure called Fast Update Frequent Pattern Tree (FUFP-Tree) that reduce the number of traversing between tree nodes in both inserting a new transaction and extracting an association rules between items. Also, to avoid congestion during inserting incoming streaming data to FUFP-Tree, a sliding window approach is used to divide incoming data equally to all available windows. The complexity and the performance of this technique are investigated, and a dataset of storehouse is used to test the proposed technique and measure its efficiency. The efficiency of the proposed technique is compared with other most related algorithms.

A Hybrid Behavioural Based Cyber Intrusion Detection System

The experience of deploying intrusion detection system (IDS) for securing computer system is being matured. There are knowledge-based (misuse) and anomaly IDS. In knowledge-based IDS, prior knowledge of the attack is needed for detection and during anomaly, behaviour of normal data is studied, when new data is arrived and there is a deviation, it is considered as an attack. In this thesis, we present a hybrid intrusion detection system called behavioural-based cyber intrusion detection system, based on two data mining algorithms, decision tree and association rule mining. The decision tree algorithm is used to detect misuse intrusions but it considers new attacks as normal. Association rule mining works by using the normal output of decision tree as input for further detection. Further, we implement the proposed model using java programming language. We have used a reduced and enhanced non-redundant NSL_KDD dataset for training and testing. Evaluation results show that it provides improved detection rate and lower false alarm rates.

Fast mining algorithm for multi-level association rule data under temporal constraints

Redundant interference occurs between frames of multi-level association rule data under temporal constraints, which brings poor clustering and anti-interference performance to data mining. In order to improve the multi-level association rule data mining ability, this paper proposes a fast mining algorithm for multi-level association rule data based on temporal constraints. It constructs a fitting state model of multi-level association data distribution, and uses the reorganisation method of multi-level association rules to re-arrange data structure and extract the average mutual information feature; it constructs detection statistics to conduct multi-level linear programming design for association rules data, and uses the autocorrelation detection method to conduct de-interference processing and the fuzzy directional clustering method to conduct fuzzy clustering processing for multi-level association rule data, to realise fast mining of multi-level association rule data under temporal constraints. The simulation results show that compared with traditional methods, the proposed method reduces the execution time of multi-level association rule data mining by 12.77%, and the mining accuracy is improved by 23.34%. High mining accuracy and strong anti-interference ability make the data mining efficiency improved.

Fast mining algorithm for multi-level association rule data under temporal constraints

Redundant interference occurs between frames of multi-level association rule data under temporal constraints, which brings poor clustering and anti-interference performance to data mining. In order to improve the multi-level association rule data mining ability, this paper proposes a fast mining algorithm for multi-level association rule data based on temporal constraints. It constructs a fitting state model of multi-level association data distribution, and uses the reorganisation method of multi-level association rules to re-arrange data structure and extract the average mutual information feature; it constructs detection statistics to conduct multi-level linear programming design for association rules data, and uses the autocorrelation detection method to conduct de-interference processing and the fuzzy directional clustering method to conduct fuzzy clustering processing for multi-level association rule data, to realise fast mining of multi-level association rule data under temporal constraints. The simulation results show that compared with traditional methods, the proposed method reduces the execution time of multi-level association rule data mining by 12.77%, and the mining accuracy is improved by 23.34%. High mining accuracy and strong anti-interference ability make the data mining efficiency improved.

Using Data Mining Algorithms to Discover Regular Sound Changes among Languages

This paper presents a method of using association rule data mining algorithms to discover regular sound changes among languages. The method presented has a great potential to facilitate linguistic studies aimed at identifying distantly related cognate languages. As an experimental example, this paper presents the application of the data mining method to the discovery of regular sound changes between the Hungarian and the Sumerian languages, which separated at least five thousand years ago when the Proto-Sumerian reached Mesopotamia. The data mining method discovered an important regular sound change between Hungarian word initial /f/ and Sumerian word initial /b/ phonemes.

Bacterial Colony Algorithms for Association Rule Mining in Static and Stream Data

Bacterial colonies perform a cooperative and distributed exploration of the environmental resources by using their quorum-sensing mechanisms. This paper describes how bacterial colony networks and their skills to explore resources can be used as tools for mining association rules in static and stream data. A new algorithm is designed to maintain diverse solutions to the problems at hand, and its performance is compared to that of other well-known bacteria, genetic, and immune-inspired algorithms: Bacterial Foraging Optimization (BFO), a Genetic Algorithm (GA), and the Clonal Selection Algorithm (CLONALG). Taking into account the superior performance of our approach in static data, we applied the algorithms to dynamic environments by converting static into flow data via a stream data model named sliding-window. We also provide some notes on the running time of the proposed algorithm using different hardware and software architectures.

Data Mining and Business Process Management of Apriori Algorithm

The classical association rule data mining algorithm Apriori algorithm is widely used in various fields. Through the analysis and mining of data relevance, the information extracted has important reference value in the decision-making process. The continuous promotion and application of information technology, how to make full use of this data information to provide decision support for decision maker in various industries have become an urgent and thorny issue. In addition to using the existing relational database standard query statements to obtain general and intuitive information, It is necessary to mine the data relationships that are contained, unknown, and actually exist. The famous Apriori algorithm is an algorithm for mining association rules. In this paper, through the basic idea of Apriori algorithm, the embedded data relationship is mined and Apriori algorithm is implemented.

PO-088 Effect of Aerobic Exercise on Myocardial Mitochondria Fusion-Fission in Chronic Intermittent Hypoxia Mice

Objective Training monitoring is an important part of scientific training, and also accumulated a large amount of data, but the analysis and evaluation of biochemical indicators are mostly concentrated on the level of experience and the general, phased and individualized research application of statistical methods. The data mining technology is applied to the analysis and evaluation of the biochemical indexes of competitive sports, the analysis of the data is carried out in the deep level, the potential, new and useful information and knowledge are extracted, and the new exploration ideas are carried out for the analysis of the biochemical indexes of competitive sports, and a more reliable and more powerful data branch is provided for the scientific and efficient training support.
 Methods Using the literature data method, logic analysis method and expert interview method, the application of the current data mining technology in the analysis of biochemical indicators is summarized.
 Results The scientific analysis and evaluation of athletes' physical function status has been the focus of domestic and foreign coaches and sports researchers. The application of data mining technology in sports biochemical indicators is also becoming more and more extensive. For example, Mao Jie and others applied the gray ART clustering model analysis method to the monitoring of competitive sports biochemical indicators. Through this data mining model, the coach can easily judge the athlete's competitive physical condition, and can provide a scientific basis for correct training according to the different competitive conditions of each athlete, using different training guidance programs and training methods. Ma Jing et al. explored the feasibility of applying decision tree algorithm and association rules in volleyball biochemical analysis. It was found that C5.0 decision tree and Apriori association rule algorithm can be used to predict and analyze the technical level of women's volleyball players. Li Guangjun and others successfully applied the association rule data mining to the biochemical data analysis of canoeists, and provided a basis for scientific decision-making and analysis of sports training and athlete selection. Zhang Hui designed a data mining system for sports biochemical index based on association rules. The results show that the system has fast data mining rate, short time consuming and high reliability. It provides a more scientific evaluation standard for the data mining of sports biochemical index, and also provides a basis for the future training program.
 Conclusions With the development of competitive sports, in order to achieve new heights, the application of data mining technology to vast biochemical data is of great significance for the establishment of scientific training evaluation methods and standards, and is also the inevitable development of future sports scientific research.

An outcome-based process optimization model using fuzzy-based association rules

PurposeThe purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment.Design/methodology/approachThis model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes.FindingsThe new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case example, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome.Research limitations/implicationsThe proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results.Originality/valueRecent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control.

Indexing Arabic texts using association rule data mining

PurposeThe purpose of this paper is to propose a new model to enhance auto-indexing Arabic texts. The model denotes extracting new relevant words by relating those chosen by previous classical methods to new words using data mining rules.Design/methodology/approachThe proposed model uses an association rule algorithm for extracting frequent sets containing related items – to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The associations of words extracted are illustrated as sets of words that appear frequently together.FindingsThe proposed methodology shows significant enhancement in terms of accuracy, efficiency and reliability when compared to previous works.Research limitations/implicationsThe stemming algorithm can be further enhanced. In the Arabic language, we have many grammatical rules. The more we integrate rules to the stemming algorithm, the better the stemming will be. Other enhancements can be done to the stop-list. This is by adding more words to it that should not be taken into consideration in the indexing mechanism. Also, numbers should be added to the list as well as using the thesaurus system because it links different phrases or words with the same meaning to each other, which improves the indexing mechanism. The authors also invite researchers to add more pre-requisite texts to have better results.Originality/valueIn this paper, the authors present a full text-based auto-indexing method for Arabic text documents. The auto-indexing method extracts new relevant words by using data mining rules, which has not been investigated before. The method uses an association rule mining algorithm for extracting frequent sets containing related items to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The benefits of the method are demonstrated using empirical work involving several Arabic texts.

Usage Apriori and clustering algorithms in WEKA tools to mining dataset of traffic accidents

ABSTRACTThe aim of this study is finding approaches for investigating association rules mining algorithms and clustering to offer new rules from a broad set of discovered rules which taken from traffic accident data at Alghat Provence in KSA. Several tools are applying in data mining to extracting data. WEKA provides applications of learning algorithms that can efficiently execute any dataset. In WEKA tools, there are many algorithms used to mining data. Apriori and cluster are the first-rate and most famed algorithms. Apriori is the simple algorithm, which applied for mining of repeated the patterns from the transaction dataset to find frequent itemsets and association between various item sets. A cluster is a technique used to group a collection of items having similar features. Association rules applied to find the connection between data items in a transactional database. Association rules data mining algorithms used to discover frequent association. WEKA tools were used to analysing traffic dataset, which composed of 946 instances and 8 attributes. Apriori algorithm and EM cluster were implemented for traffic dataset to discover the factors, which causes accidents. Through the results, shows that the Apriori algorithm is better than the EM cluster algorithm.

A Study on Basketball Techniques and Tactics Based on Apriori Algorithm

At present, the informatization of basketball matches is gradually increasing. Massive data accumulated in the basketball database can be effectively used through data mining. This study analyses basketball techniques and tactics based on the Apriori algorithm. First, this study introduces Apriori algorithm in data mining of association rules. Then, based on Apriori algorithm, this study explores most commonly used technical actions in basketball games to provide reference for the training of players and coaches. Improved algorithms and data of basketball games were used to make association rules analysis to analyze technical and tactical characteristics. The algorithm generates association rules based on the frequent itemsets of basketball technical moves. The improved algorithm can dig out strongly connected technical actions in basketball games. The analysis has significantly guiding function.

Impact of grouping complications on mortality in traumatic brain injury: A nationwide population-based study.

Traumatic brain injury (TBI) is an important health issue with high mortality. Various complications of physiological and cognitive impairment may result in disability or death after TBI. Grouping of these complications could be treated as integrated post-TBI syndromes. To improve risk estimation, grouping TBI complications should be investigated, to better predict TBI mortality. This study aimed to estimate mortality risk based on grouping of complications among TBI patients. Taiwan's National Health Insurance Research Database was used in this study. TBI was defined according to the International Classification of Diseases, Ninth Revision, Clinical Modification codes: 801–804 and 850–854. The association rule data mining method was used to analyze coexisting complications after TBI. The mortality risk of post-TBI complication sets with the potential risk factors was estimated using Cox regression. A total 139,254 TBI patients were enrolled in this study. Intracerebral hemorrhage was the most common complication among TBI patients. After frequent item set mining, the most common post-TBI grouping of complications comprised pneumonia caused by acute respiratory failure (ARF) and urinary tract infection, with mortality risk 1.55 (95% C.I.: 1.51–1.60), compared with those without the selected combinations. TBI patients with the combined combinations have high mortality risk, especially those aged <20 years with septicemia, pneumonia, and ARF (HR: 4.95, 95% C.I.: 3.55–6.88). We used post-TBI complication sets to estimate mortality risk among TBI patients. According to the combinations determined by mining, especially the combination of septicemia with pneumonia and ARF, TBI patients have a 1.73-fold increased mortality risk, after controlling for potential demographic and clinical confounders. TBI patients aged<20 years with each combination of complications also have increased mortality risk. These results could provide physicians and caregivers with important information to increase their awareness about sequences of clinical syndromes among TBI patients, to prevent possible deaths among these patients.

The Hierarchies of Multivalued Attribute Domains and Corresponding Applications in Data Mining

In mobile computing, machine learning models for natural language processing (NLP) have become one of the most attractive focus areas in research. Association rules among attributes are common knowledge patterns, which can often provide potential and useful information such as mobile users′ interests. Actually, almost each attribute is associated with a hierarchy of the domain. Given an relation R = (U, A) and any cut αa on the hierarchy for every attribute a, there is another rough relation RΦ, where Φ = (αa : a ∈ A). This paper will establish the connection between the functional dependencies in R and RΦ, propose the method for extracting reducts in RΦ, and demonstrate the implementation of proposed method on an application in data mining of association rules. The method for acquiring association rules consists of the following three steps: (1) translating natural texts into relations, by NLP; (2) translating relations into rough ones, by attributes analysis or fuzzy k‐means (FKM) clustering; and (3) extracting association rules from concept lattices, by formal concept analysis (FCA). Our experimental results show that the proposed methods, which can be applied directly to regular mobile data such as healthcare data, improved quality, and relevance of rules.