Association Rule Mining

This paper presents an intelligent prefetching technique that significantly improves hybrid flash-disk storage, a combination of hard disk and flash memory. As a prefetching strategy, we adopt the sequential pattern mining, a variant of association rule mining. Our goal is to minimize overall I/O processing time of hybrid storage systems with using the Fully Associated Sector Translation (FAST) technique that

Dividing a data set into a training set and a test set is a fundamental component in the preprocessing phase of data mining (DM). Effectively, the choice of the training set is an important factor in deriving good classification rules. Traditional approach for association rules mining divides the dataset into training set and test set based on statistical methods. In

In recent years, it has been noticed that women are making progress in every sector of society. Their involvement in every field, such as education, job market, social work, etc., is increasing at a remarkable rate. For the last several years, the government has been trying its level best for the advancement of women in every sector by doing several research work and activities and funding several organizations to motivate women. Although women's involvement in several fields is increasing, the big concern is they are facing several barriers in their advancement, and it is not surprising that sexual harassment is one of them. In Bangladesh, harassment against women, especially students, is a common phenomenon, and it is increasing. In this paper, a survey-based and Apriori algorithm are used to analyze the several impacts of harassment among several age groups. Also, several factors such as frequent impacts of harassment, most vulnerable groups, women mostly facing harassment, the alleged person behind harassment, etc., are analyzed through association rule mining of Apriori algorithm and F.P. Growth algorithm. And then, a comparison of performance between both algorithms has been shown briefly. For this analysis, data have been carefully collected from all ages.

Salah satu metode data mining yang cukup popular untuk mendapatkan hubungan antar sebuah item dengan item tertentu adalah metode aturan asosiasi dengan menggunakan algoritma APriori, metode ini cukup tepat untuk menghasilkan pola aturan hubungan antar jenis item yang terjual berdasarkan data penjualan. Nilai support pada frequent item dan confidence pada rules yang didapat bisa memberikan wawasan yang dapat ditindaklanjuti oleh pengelola minimarket, koperasi dan lain sebagainya. Pengkategorian jenis produk pada minimarket cukup banyak sementara jumlah keseluruhan transaksi dalam satu tahun juga sangat banyak, sedangkan jumlah jenis item terjual dalam suatu transaksi sangat sedikit, dengan demikian nilai threshold tidak bisa terlampau tinggi. Pada penelitian ini metode aturan asosiasi dilakukan per event atau periode tertentu yang berkaitan dengan hari besar umat Islam, didapatkan rule tertinggi yaitu makanan ringan => sembako dengan confidence sebesar 46% dan support sebesar 16% yang terjadi pada bulan Ramadhan.

RapidMiner is a software for machine learning, data mining, predictive analytics, and business analytics. The server will record large web log files when user visits the website. Extracting knowledge from such huge data demands for new methods. In this paper, we propose a web usage mining method with RapidMiner. At first, the redundant files in log file are deleted by Matlab and then we mines web log which has been pretreated with RapidMiner, it obtains the custom of different user to visit the website by processing and analyzing log file, and mines unusual rules, and provides the reference for the policy decision and construction of website. Experimental result analysis show that, applying RapidMiner in web usage mining, will obtain frequent model which user visits the website, manage to optimize the website structure and recommends for users.

Accidents happen unpredictably and involuntarily that usually cause damage or injury or fatalities. Data mining has proven to be a dependable technique for analyzing traffic accidents and results. In recent advances, data mining has taken great steps in extracting latent models and new data from large datasets often ignored by conventional statistical methods. In this study, we have identified more accurate and useful patterns that appear in traffic accident data using most decision tree induction algorithms. These patterns exercised to minimize the number of accidents or diminished the severity of an accident. As part of this research work, details of accidents occurred in Dhaka-Banglabandha (N5) National Highway of Bangladesh in the year 2007-2015 were collected from ARI (Accident Research Institute), BUET (Bangladesh University of Engineering and Technology), Bangladesh. We have analyzed 1283 traffic accident incidents through 28 features generated from the collected data. Also, we have found a few classifiers to make use of various decision tree algorithms. In this experimental analysis, we have measured the performance of 10 decision tree classifiers and also compared the error rate of the following classifiers and found J48 is the best classifier. Finally, we have extracted rules for the trees, which can predict the type of accident based on the identified attribute values. The results help to point out the factors that affect traffic accidents and analyze the more precise causes or situations of occurred accidents.

Data Mining refers to the process of mining useful data over large datasets. The discovery of interesting association relationships among large amounts of business transactions is currently vital for making appropriate business decisions. This is the reason that the research in data mining is carried out largely for business decision making rather than for academic importance. Association rule analysis is the task of discovering association rules that occur frequently in a given transaction data set. Its task is to find certain relationships among a set of data (itemset) in the database. It has two measurements: Support and confidence values. Confidence value is a measure of rule’s strength, while support value corresponds to statistical significance. There are currently a variety of algorithms to discover association rules. Most of the algorithms need a specification of minimum support value as user input. Specifying minimum support values of items is not recommended as it leads to very less or very large rules. With a sufficiently high support value, the less frequent elements gets eliminated, leaving only the elements which are most frequent. Thus, knives and spoons may get eliminated leaving only biscuits and milk. One approach for this problem is proposed by MsApriori Algorithm. However, both Apriori and MsApriori are computationally complex and need large computational time for large datasets over traditional machines. One solution to this problem is proposed by Dynamic Matrix Apriori which is much faster as compared to traditional Apriori in the generation of candidate sets. The contribution of this paper is twofold. It first proposed a method to use MsAprioiri using Dynamix Matrix Technique. It then proposes a framework to use the Algorithm under the Map Reduce Programming model. Experiments on large set of data bases have been conducted to validate the proposed framework. The achieved results show that there is a remarkable improvement in the overall performance of the system in terms of run time, the number of generated rules, and number of frequent items used.

Association rule mining is a significant research topic in the knowledge discovery area. In the last years a great number of algorithms have been proposed with the objective of solving diverse drawbacks presented in the generation of association rules. One of the main problems is to obtain interesting rules from continuous numeric attributes. In this paper, a method for mining

Data mining can be defined as the process of uncovering hidden patterns in random data that are potentially useful. The discovery of interesting association relationships among large amounts of business transactions is currently vital for making appropriate business decisions. Association rule analysis is the task of discovering association rules that occur frequently in a given transaction data set. Its task is to find certain relationships among a set of data (itemset) in the database. It has two measurements: Support and confidence values. Confidence value is a measure of rule’s strength, while support value corresponds to statistical significance. There are currently a variety of algorithms to discover association rules. Some of these algorithms depend on the use of minimum support to weed out the uninteresting rules. Other algorithms look for highly correlated items, that is, rules with high confidence. Traditional association rule mining techniques employ predefined support and confidence values. However, specifying minimum support value of the mined rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system. This work proposes a way to efficiently mine association rules over dynamic databases using Dynamic Matrix Apriori technique and Multiple Support Apriori (MSApriori). A modification for Matrix Apriori algorithm to accommodate this modification is proposed. Experiments on large set of data bases have been conducted to validate the proposed framework. The achieved results show that there is a remarkable improvement in the overall performance of the system in terms of run time, the number of generated rules, and number of frequent items used.

Sustaining the students' healthy canteen food and drinks should be a priority in every school. Students share the same predicaments as school canteens serve the same food every day without considering the choice of healthy foods that the students desire. This study employed the Knowledge Discovery on Data (KDD) technique. Ninety-seven (97) out of one hundred thirty (130) junior high school students were the respondents of the study. WEKA data mining tool was used to determine the most preferred foods by the students. The data were analyzed using R programming applying the Apriori algorithm to generate the association rules. The result shows that considering the food preference of the students is the ideal way to improve the school canteen management. The study also revealed that students prefer to eat fruits like apple, orange, mango, and meat such as chicken and pork. Moreover, fruit juices were the most preferred refreshment than water. Biscuits and breads can be served also as snacks. It was discovered that pork and chicken can be partnered to moringa leaves and squash based on the result of the association rules. Health initiative programs can be implemented in the school such as menu alterations in the school canteen based on the food preference, orienting the parents, and implementing a strict policy in selling foods or snacks within the school premises. Since schools play an essential role in influencing the students' health well-being. Therefore, school canteens should consider locally based and affordable food preference

Data mining is the task of discovering interesting patterns from large amounts of data. There are many data mining tasks, such as classification, clustering, association rule mining, and sequential pattern mining. Sequential pattern mining finds sets of data items that occur together frequently in some sequences. Sequential pattern mining, which extracts frequent subsequences from a sequence database, has attracted a great deal of interest during the recent data mining research because it is the basis of many applications, such as: web user analysis, stock trend prediction, DNA sequence analysis, finding language or linguistic patterns from natural language texts, and using the history of symptoms to predict certain kind of disease. The diversity of the applications may not be possible to apply a single sequential pattern model to all these problems. Each application may require a unique model and solution. A number of research projects were established in recent years to develop me...

Rare association rules are those that only appear infrequently even though they are highly associated with very specific data. In consequence, these rules can be very appropriate for using with educational datasets since they are usually imbalanced. In this paper, we explore the extraction of rare association rules when gathering student usage data from a Moodle system. This type of rule is more difficult to find when applying traditional data mining algorithms. Thus we show some relevant results obtained when comparing several frequent and rare association rule mining algorithms. We also offer some illustrative examples of the rules discovered in order to demonstrate both their performance and their usefulness in educational environments.

Most of the existing association rule mining algorithms are able to extract knowledge from databases with attributes of binary values. However, in real-world applications, databases are usually composed of continuous values such as height, length or weight. If the attributes are continuous, the algorithms are commonly integrated with a discretization method that transforms them into discrete attributes. Discretization is a process of transforming a continuous attribute value into a finite number of intervals and assigning each interval into a discrete numerical value. However, the user most often must specify the number of intervals, or provide some heuristic rules to be used while discretization, and then it is difficult to get the highest attribute interdependency and at the same time get the lowest number of intervals. In this paper we present an association rule mining algorithm that is suited for continuous valued attributes commonly found in scientific and statistical databases. We propose a method using a new graph-based evolutionary algorithm named ‘genetic network programming (GNP)’ that can deal with continuous values directly, that is, without using any discretization method as a preprocessing step. GNP represents its individuals using graph structures and evolves them in order to find a solution; this feature contributes to creating very compact programs and implicitly memorizing past action sequences. In the proposed method using GNP, the significance of the extracted association rules is measured by the use of χ2 test, and only important association rules are stored in a pool all together through generations. Results of experiments conducted on a real-life database suggest that the proposed method provides an effective technique for handling continuous attributes. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Data mining is a technique that has become a widely accepted procedure for organizations in sourcing for data and processing it for decision making. Association rule mining is an aspect of data mining that has revolutionized the area of predictive modelling paving way for data mining technique to become the recommended method for business owners to evaluate organisational performance. Association rule mining (ARM) give top managers the opportunity to make informed business decisions by anticipating future movements and behaviours of customers. Market basket analysis (MBA) is paving the path in business as it has become the most widely used areas of data mining in marketing. This study defines association rule mining as a technique used to extract important patterns from existing information which enables better decision making in an establishment. MBA is a marketing strategy used by various organizations to find the optimal environments to advertise merchandise. A market basket comprises of products picked by a customer during the visit to a superstore. These work specifically focus on association rule mining algorithms and its application to MBA. This paper presents a critical review of various ARM algorithms, comparing each of the algorithms, and considering the merit and demerit of each. The outcome of the study shows that choosing an ARM algorithm for MBA depends on the data set size and the application area of MBA that the algorithm will be used, this is because according to the no free lunch theorem which state that no algorithm is guaranteed to outperform others in all domains hence the need for this study, to determine the performance of the algorithms. The study concluded by recommending a hybrid algorithm to be used for ARM in MBA systems.