Association Rule

In recent years, Association Rule Discovery has become a core topic in Data Mining. It attracts more attention because of its wide applicability. Association rule mining is normally performed in generation of frequent itemsets and rule generation in which many researchers presented several efficient algorithms. This paper aims at giving a theoretical survey on some of the existing algorithms. The concepts behind association rules are provided at the beginning followed by an overview to some of the previous research works done on this area. The advantages and limitations are discussed and concluded with an inference.

Recommendation systems are widely used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days are competing with each other by many means. Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyer’s interest. This paper presents book recommendation system based on combined features of content filtering, collaborative filtering and association rule mining.

Decision making and understanding the behavior of the customer has become vital and challenging problem for organizations to sustain their position in the competitive markets. Technological innovations have paved breakthrough in faster processing of queries and sub-second response time. Data mining tools have become surest weapon for analyzing huge amount of data and breakthrough in making correct decisions. The objective of this paper is to analyze the huge amount of data thereby exploiting the consumer behavior and make the correct decision leading to competitive edge over rivals. Experimental analysis has been done employing association rules using Market Basket Analysis to prove its worth over the conventional methodologies.

Analyzing bank databases for customer behavior management is difficult since bank databases are multi-dimensional, comprised of monthly account records and daily transaction records. This study proposes an integrated data mining and behavioral scoring model to manage existing credit card customers in a bank. A self-organizing map neural network was used to identify groups of customers based on repayment behavior and recency, frequency, monetary behavioral scoring predicators. It also classified bank customers into three major profitable groups of customers. The resulting groups of customers were then profiled by customer's feature attributes determined using an Apriori association rule inducer. This study demonstrates that identifying customers by a behavioral scoring model is helpful characteristics of customer and facilitates marketing strategy development. q

Association rules are considered to be the best studied models for data mining. In this article, we propose their use in order to extract knowledge so that normal behavior patterns may be obtained in unlawful transactions from transactional credit card databases in order to detect and prevent fraud. The proposed methodology has been applied on data about credit card fraud in some of the most important retail companies in Chile.

Nowadays, the application of data mining techniques in e-learning and web based adaptive educational systems is increasing exponentially. The discovered useful information can be used directly by the teacher or the author of the course to improve the instructional/learning performance. This can be an arduous task and therefore educational recommender systems are used in order to help the teacher in this task. In this paper we describe a recommender system oriented to suggest the most appropriate modifications to the teacher in order to improve the effectiveness of the course. We propose to use a cyclical methodology to develop and carry out the maintenance of web-based courses in which we have added a specific data mining step. We have developed a distributed rule mining system in order to discover information in the form of IF-THEN recommendation rules about the web courses. We have used an iterative and interactive association rule algorithm without parameters and with a weight-based evaluation measure of the rule interest. And we have used a collaborative recommender system to share and score the obtained recommendation rules in one specific course between teachers of other similar courses and some experts in education. Finally, we have carried out several experiments with real students in order to determine the effectiveness of the proposed system and the utility of the recommended rules.

Currently, tax authorities face the challenge of identifying and collecting from businesses that have successfully evaded paying the proper taxes. In solving the problem of tax evaders, tax authorities are equipped with limited resources and traditional tax auditing strategies that are time-consuming and tedious. These continued practices have resulted in the loss of a substantial amount of tax revenue for the government. The objective of the current study is to apply a data mining technique to enhance tax evasion detection performance. Using a data mining technique, a screening framework is developed to filter possible non-compliant value-added tax (VAT) reports that may be subject to further auditing. The results show that the proposed data mining technique truly enhances the detection of tax evasion, and therefore can be employed to effectively reduce or minimize losses from VAT evasion.

There is a growing recognition of the need to understand the impacts of culture and ethnicity on sport consumption, and to identify the consequent implications for sport management and marketing. Styles and patterns of sport involvement vary across cultures, sometimes requiring that associated rules, rituals, and ceremonies be modified or adapted to fit particular cultural settings. Patterns and styles of interpersonal communication also vary across cultures. Two dimensions of cultural difference in interpersonal communication -inclusion versus exclusion and egalitarianism versus respect -have particular potential to affect sport participation negatively if they are not accommodated appropriately. Depending on their sensitivity to cultural differences, the policies and practices of sport managers in multicultural settings can enhance or worsen inter-ethnic relations, recruitment, turnover, motivation, and involvement. Needed research and implications for the management of sport are highlighted.

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, however, transactions with quantitative values are commonly seen in real-world applications.

Recommendation Systems are changing from novelties which were used by a few E-commerce sites, to tools that are almost-shaping the world of E-commerce. Many of the largest commerce web sites are already using recommendation systems to encourage their customers, locate products. A recommendation system learns from a customer and recommends products that he/she will investigate most necessary from the available products. This research work is to recommend a product using Apriori algorithm. This algorithm is mainly used to find frequently purchased items/products. Its aim is to detect association rules.

Dalam pembangunan perikanan laut, penguasaan teknologi perlu ditingkatkan. Selain itu, juga perlu diimbangi dengan sistem informasi dan data yang akurat bagi kepentingan nelayan maupun instansi terkait untuk pengambilan kebijakan. Misalnya informasi mengenai daerah penangkapan ikan dan potensi sumberdaya ikan di suatu perairan tertentu sehingga informasi-informasi ini dapat mengarahkan nelayan melakukan penangkapan. Untuk mengatasi masalah dalam pemasaran produk perikanan, diperlukan analisa terhadap pola data hasil tangkapan ikan di setiap daerah penangkapan ikan, sehingga dapat diketahui pada bulan tertentu ikan apa yang banyak dihasilkan oleh nelayan di masing-masing daerah penangkapan ikan. Dengan demikian, kita dapat menentukan daerah mana yang akan dijadikan sebagai tujuan distribusi dan pemasaran ikan yang dihasilkan. Dalam penelitian ini digunakan metode association rule (aturan asosiatif) yang merupakan salah satu teknik utama dalam data mining dan merupakan bentuk yang paling umum dipakai dalam menemukan pattern atau pola dari suatu kumpulan data. Sedangkan algoritma yang digunakan adalah algoritma Apriori. Dalam penelitian ini dilakukan pengujian pada data tangkapan ikan dengan atribut nama daerah, jenis ikan, jumlah ikan, dan waktu penangkapan.

Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures, from large amounts of data stored in databases, data warehouses, or other information repositories. Mining Associations is one of the techniques involved in the process mentioned above and used in this paper. Association is the discovery of association relationships or correlations among a set of items. The algorithm that was implemented is a basic algorithm for mining association rules, known as a priori. In Healthcare, association rules are considered to be quite useful as they offer the possibility to conduct intelligent diagnosis and extract invaluable information and build important knowledge bases quickly and automatically. The problem of identifying new, unexpected and interesting patterns in medical databases in general, and diabetic data repositories in specific, is considered in this paper. We have applied the apriori algorithm to a database containing records of diabetic patients and attempted to extract association rules from the stored real parameters. The results indicate that the methodology followed may be of good value to the diagnostic procedure, especially when large data volumes are involved. The followed process and the implemented system offer an efficient and effective tool in the management of diabetes. Their clinical relevance and utility await the results of prospective clinical studies currently under investigation.

Spatio-temporal data sets are often very large and difficult to analyze and display. Since they are fundamental for decision support in many application contexts, recently a lot of interest has arisen toward data-mining techniques to filter out relevant subsets of very large data repositories as well as visualization tools to effectively display the results. In this paper we propose a data-mining system to deal with very large spatio-temporal data sets. Within this system, new techniques have been developed to efficiently support the data-mining process, address the spatial and temporal dimensions of the data set, and visualize and interpret results. In particular, two complementary 3D visualization environments have been implemented. One exploits Google Earth to display the mining outcomes combined with a map and other geographical layers, while the other is a Java3Dbased tool for providing advanced interactions with the data set in a non-geo-referenced space, such as displaying association rules and variable distributions. r

In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.

The paper presents the implementation of an association rules discovery data mining task using Grid technologies. For the mining task we are using the Apriori algorithm on top of the Globus toolkit. The case study presents the design and integration of the data mining algorithm with the Globus services. The paper compares the Grid version with related work in the field and we outline the conclusions and future work.

Association Rule Mining among Frequent Items has been widely studied in Data Mining. Many researchers have improved the algorithm for generation of all the Frequent Itemsets. Frequent Itemset mining plays an essential role in Data Mining. Various algorithms have been proposed to generate all large frequent itemsets from a large amount of transaction data using graphs. In this paper we generally review and compare the most important graph based algorithms with each other. Results shows that each algorithm based on its applied strategy has some advantages and some disadvantages. However compress and mine algorithm is more effective and takes less time and space. In this paper we discuss various approaches to find Frequent Itemsets using Graphs.

Abstract: Educational Data Mining (EDM) is an rising ground search data in educational background by relating different Data Mining (DM) techniques/tools. It gives basic knowledge of teaching and learning method for useful education development. Educational data mining (also referred to as “EDM”) is clear as the area of scientific query centered around the growth of methods for making discoveries within the single kinds of data that come from educational background, and using those technique to better identify students and the background which they be trained in. This boost in rapidity and feasibility has had the advantage of making imitation much more possible. This paper addresses the purpose of data mining in educational society to pull out useful information from the vast data sets and providing logical tool to analysis and use this information for decision making processes by taking real life examples.
Keywords: Educational Data Mining (EDM); Classification; Knowledge Discovery in Database (KDD); ID3 Algorithm. Higher education,, Data mining, Knowledge discover, Classification, Association rule, Prediction.
Title: A Review on Role of Data Mining Techniques in Enhancing Educational Data to Analyze Student’s Performance
Author: Dr N. Preethi, Deepak Goswami
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Research Publish Journals

The task of mining association rules consists of two main steps. The first involves finding the set of all frequent itemsets. The second step involves testing and generating all high confidence rules among itemsets. In this paper we show that it is not necessary to mine all frequent itemsets in the first step, instead it is sufficient to mine the set of closed frequent itemsets, which is much smaller than the set of all frequent itemsets. It is also not necessary to mine the set of all possible rules. We show that any rule between itemsets is equivalent to some rule between closed itemsets. Thus many redundant rules can be eliminated. Furthermore, we present CHARM, an efficient algorithm for mining all closed frequent itemsets. An extensive experimental evaluation on a number of real and synthetic databases shows that CHARM outperforms previous methods by an order of magnitude or more. It is also linearly scalable in the number of transactions and the number of closed itemsets found.

In this paper, we introduce a novel technique, called F-APACS, for mining fuzzy association rules. Existing algorithms involve discretizing the domains of quantitative attributes into intervals so as to discover quantitative association rules. These intervals may not be concise and meaningful enough for human experts to easily obtain nontrivial knowledge from those rules discovered.

To engage visitors to a Web site at a very early stage (i.e., before registration or authentication), personalization tools must rely primarily on clickstream data captured in Web server logs. The lack of explicit user ratings as well as the sparse nature and the large volume of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance. Web usage mining techniques such as clustering that rely on offline pattern discovery from user transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of reduced recommendation accuracy. In this paper we propose effective and scalable techniques for Web personalization based on association rule discovery from usage data. Through detailed experimental evaluation on real usage data, we show that the proposed methodology can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy.

Classification is considered as one of the
building blocks in data mining problem and the major
issues concerning data mining in large databases are
efficiency and scalability. In this paper we propose a data
classification method using AVL trees enhances the
quality and stability of data mining problems.
Researchers from various disciplines such as statistics,
machine learning, pattern recognition, and data mining
considered the issue of growing a decision tree from
available data. Specifically, we consider a scenario in
which we apply the multi level mining method on the
data set and show how the proposed approach tend to
give the efficient multiple level classifications of large
amounts of data. The results specify that performance
evaluation of the proposed algorithm that uses the
algorithm to acquire designing rule from the knowledge
database is discussed in the paper.

A large volume of research in temporal data mining is focusing on discovering temporal rules from time-stamped data. The majority of the methods proposed so far have been mainly devoted to the mining of temporal rules which describe relationships between data sequences or instantaneous events and do not consider the presence of complex temporal patterns into the dataset. Such complex patterns, such as trends or up and down behaviors, are often very interesting for the users. In this paper we propose a new kind of temporal association rule and the related extraction algorithm; the learned rules involve complex temporal patterns in both their antecedent and consequent. Within our proposed approach, the user defines a set of complex patterns of interest that constitute the basis for the construction of the temporal rule; such complex patterns are represented and retrieved in the data through the formalism of knowledge-based Temporal Abstractions. An Apriori-like algorithm looks then for meaningful temporal relationships (in particular, precedence temporal relationships) among the complex patterns of interest. The paper presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical variables during hemodialysis sessions, while the other one deals with the biological problem of inferring relationships between genes from DNA microarray data.

... A data mining approach was used to select and prioritize a set of customers, eg, a top-of-the-range product for high income customers. Finally, the standardization of processes was considered for improving the efficiency of the manufacturing process to produce new parts. ...

Data mining is gaining importance due to huge amount of data available. Retrieving information from the warehouse is not only tedious but also difficult in some cases. The most important usage of data mining is customer segmentation in marketing, shopping cart analyzes, management of customer relationship, campaign management, Web usage mining, text mining, player tracking and so on. In data mining, association rule mining is one of the important techniques for discovering meaningful patterns from large collection of data. Discovering frequent itemsets play an important role in mining association rules, sequence rules, web log mining and many other interesting patterns among complex data. This paper presents a literature review on different techniques for mining frequent itemsets.

On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge validation is one of the most problematic steps in an association rule discovery process. In order to comprehend this bulk of rules and to find relevant knowledge for decision-making, the user needs to really rummage through the rules. Visualization can be very beneficial to support him/her in this task by improving the intelligibility of the large rule sets and enabling the user to carry out his/her navigation inside them. In this article, we propose to answer the association rule validation problem by designing a visualization for the rule rummaging task. This new approach based on a specific rummaging model relies on interactive rule focusing and on rule quality measures. A first prototype implementing our representation has been developed. It allows the user to appropriate the interesting rules by navigating through a voluminous rule set by trial and error via successive limited subsets.

In this paper we describe a data mining framework for constructing intrusion detection models. The first key idea is to mine system audit data for consistent and useful patterns of program and user behavior. The other is to use the set of relevant system features presented in the patterns to compute inductively learned classifiers that can recognize anomalies and known intrusions. In order for the classifiers to be effective intrusion detection models, we need to have sufficient audit data for training and also select a set of predictive system features. We propose to use the association rules and frequent episodes computed from audit data as the basis for guiding the audit data gathering and feature selection processes. We modify these two basic algorithms to use axis attribute(s) and reference attribute(s) as forms of item constraints to compute only the relevant patterns. In addition, we use an iterative level-wise approximate mining procedure to uncover the low frequency but important patterns. We use meta-learning as a mechanism to make intrusion detection models more effective and adaptive. We report our extensive experiments in using our framework on real-world audit data.

Application of data mining techniques to the WWW (World Wide Web), referred to as Web mining, has been the focus of several recent research projects and papers. One of several possibilities can be its application to the &stance education. Taken as a whole, the emerging trends in distance education are facilitating its usability on the Internet. With the explosive growth of information sources available on the WWW, it has increasingly understood for Web mining to be suitable for keeping pace with the trends in education, such as mass customization.

OBEDECE A TU CUERPO, ¡ÁMATE! (Lisa Bourbeau) INTRODUCCIÓN "Tras quince años de investigaciones y experiencias en el campo de la metafísica, finalmente me he decidido a escribir otro libro sobre este tema. Utilizo el término metafísica en lugar de psicosomática por la siguiente razón: somático quiere decir "referente al cuerpo", y psico "referente al alma". Incluso la medicina tradicional acepta la idea de que al menos un 75% de las enfermedades son psicosomáticas, es decir, que el mal físico es originado por una causa emocional o mental. Sin embargo, el término psicosomático no suele estar bien visto. La mayoría de las personas se sienten insultadas cuando se les dice que su enfermedad es psicosomática, pues lo interpretan como si fuera imaginaria, anormal o mental, y se niegan a ver más allá del plano físico. Por lo tanto, prefiero considerar a los malestares y enfermedades desde el punto de vista metafísico, es decir, que va más allá de lo físico.

Finding frequent patterns play an important role in mining association rules, sequences, episodes, Web log mining and many other interesting relationships among data. Frequent pattern mining methods often produce a huge number of frequent itemsets that is not feasible for effective usage. The number of highly correlated patterns is usually very small and may even be one. Most of the existing frequent pattern mining techniques often require the setting of many input parameters and may involve multiple passes over the database. Minimum support is the widely used parameter in frequent pattern mining to discover statistically significant patterns. Specifying appropriate minimum support is a challenging task for a data analyst as the choice of minimum support value is somewhat arbitrary. Generally, it is required to repeatedly execute an algorithm, heuristically tuning the value of minimum support over a wide range, until the desired result is obtained, certainly, a very time-consuming process. Setting up an inappropriate minimum support may also cause an algorithm to fail in finding the true patterns. We present a novel method to efficiently retrieve top few maximal frequent patterns in order of significance without use of the minimum support parameter. Instead, we are only required to specify a more human understandable parameter, namely the desired number itemsets k. Our technique requires only a single pass over the database and generation of length two itemsets. The association ratio graph is proposed as a compact structure containing concise information, which is created in time quadratic to the size of the database. Algorithms are described for using this graph structure to discover top-most and top-k maximal frequent itemsets without minimum support threshold. To effectively achieve this, the method employs construction of an all path source-to-destination tree to discover all maximal cycles in the graph. The results can be ranked in decreasing order of significance. Results are presented demonstrating the performance advantages to be gained from the use of this approach.

Vulnerabilities in common security components such as firewalls are inevitable. Intrusion Detection Systems (IDS) are used as another wall to protect computer systems and to identify corresponding vulnerabilities. In this paper a novel framework based on data mining techniques is proposed for designing an IDS. In this framework, the classification engine, which is actually the core of the IDS, uses Association Based Classification (ABC). The proposed classification algorithm uses fuzzy association rules for building classifiers. Particularly, the fuzzy association rulesets are exploited as descriptive models of different classes. The compatibility of any new sample (which is to be classified) with different class rulesets is assessed by the use of some matching measures and the class corresponding to the best matched ruleset is declared as the label of the sample. A new method is also proposed to speed up the rule induction algorithm via reducing items that may be included in extracted rules.

The Regional Healthcare Agency (ASL) of Pavia has been maintaining a central data repository which stores healthcare data about the population of Pavia area. The analysis of such data can be fruitful for the assessment of healthcare activities. Given the crucial role of time in such databases, we developed a general methodology for the mining of Temporal Association Rules on sequences of hybrid events. In this paper we show how the method can be extended to suitably manage the integration of both clinical and administrative data. Moreover, we address the problem of developing an automated strategy for the filtering of output rules, exploiting the taxonomy underlying the drug coding system and considering the relationships between clinical variables and drug effects. The results show that the method could find a practical use for the evaluation of the pertinence of the care delivery flow for specific pathologies.

In this paper, a general mining approach based on decision trees for segmenting image data is proposed. Pixel-wise image features are extracted and transformed into a database-like table that allows existing data mining algorithms to dig out useful information. Each tuple in the table has a feature descriptor consisting of a set of feature values for a given pixel along with its label. With the feature label, we can employ the decision tree to (1) discover relationship between the attributes of pixels and their target labels, (2) build a model for image processing by using the training data set. Both experiments and theoretical analysis are performed in our research. The results show that the proposed model is very efficient and effective for image mining and image segmentation. It can also be used to develop new image processing algorithms, refine existing algorithms, or act as an effective filter.

To engage visitors to a Web site at a very early stage (i.e., before registration or authentication), personalization tools must rely primarily on clickstream data captured in Web server logs. The lack of explicit user ratings as well as the sparse nature and the large volume of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance. Web usage mining techniques such as clustering that rely on offline pattern discovery from user transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of reduced recommendation accuracy. In this paper we propose effective and scalable techniques for Web personalization based on association rule discovery from usage data. Through detailed experimental evaluation on real usage data, we show that the proposed methodology can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy.

We propose a new name entity class extraction method based on association rules. We evaluate and compare the performance of our method with the state of the art maximum entropy method. We show that our method consistently yields a higher precision at a competitive level of recall. This result makes our method particularly suitable for tasks whose requirements emphasize the quality rather than the quantity of results.

There are many theories and approaches that have been developed to find pattern and association rule. One of the methods that have been developed is apriori method. Any method previously finds itemset using graph approaches that have weakness on huge memory usage. The lack of memory will affect many item that able to process. Further, many approaches use internal data

Web-based organizations often generate and collect large volumes of data in their daily operations. Analyzing such data can help these organizations to determine the life time value of clients, design cross marketing strategies across products and services, evaluate the e ectiveness of promotional campaigns, and nd the most e ective logical structure for their Web space. This type of analysis involves the discovery of meaningful relationships from a large collection of primarily unstructured data, often stored in Web server access logs. We propose a framework for Web mining, the applications of data mining and knowledge discovery techniques to data collected in World Wide Web transactions. We present data and transaction models for various Web mining tasks such as the discovery of association rules and sequential patterns from the Web data. We also present a W eb mining system, WEBMINER, which has been implemented based upon the proposed framework, and discuss our experimental results on real-world Web data using the WEBMINER.

This paper presents a novel association rule mining (ARM)-based dissolved gas analysis (DGA) approach to fault diagnosis (FD) of power transformers. In the development of the ARM-based DGA approach, an attribute selection method and a continuous datum attribute discretization method are used for choosing user-interested ARM attributes from a DGA data set, i.e. the items that are employed to extract association rules. The given DGA data set is composed of two parts, i.e. training and test DGA data sets. An ARM algorithm namely Apriori-Total From Partial is proposed for generating an association rule set (ARS) from the training DGA data set. Afterwards, an ARS simplification method and a rule fitness evaluation method are utilized to select useful rules from the ARS and assign a fitness value to each of the useful rules, respectively. Based upon the useful association rules, a transformer FD classifier is developed, in which an optimal rule selection method is employed for selecting the most accurate rule from the classifier for diagnosing a test DGA record. For comparison purposes, five widely used FD methods are also tested with the same training and test data sets in experiments. Results show that the proposed ARM-based DGA approach is capable of generating a number of meaningful association rules, which can also cover the empirical rules defined in industry standards. Moreover, a higher FD accuracy can be achieved with the association rule-based FD classifier, compared with that derived by the other methods.

With the widespread use of Internet technology, electronic word-of-mouth [eWOM] communication through online reviews of products and services has a strong influence on consumer behavior and preferences. Although prior research efforts have attempted to investigate the behavior of users regarding the sharing of personal experiences and browsing the experiences of others online, it remains a challenge for business managers to incorporate eWOM effects into their business planning and decision-making processes effectively. Applying a newly proposed association rule mining technique, this study investigates eWOM in the context of the tourism industry using an outbound domestic tourism data set that was recently collected in Hong Kong. The complete profiles and the relations of online experience sharers and travel website browsers are explored. The empirical results are useful in helping tourism managers to define new target customers and to plan more effective marketing strategies.