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Data extraction is used to extract important data from large database which stored at the multiple sites. It may be keep in many computers covered in the some physical location or may be covered a network of computers. In this paper, we present protocol for secure extraction of association rules in horizontally distributed database. This protocol is based on the Fast Distributed Mining (FDM) algorithm,...
Web usage mining, is the method of mining for user browsing and access patterns. Usage data captures the identity or origin of Web users along with their surfing behavior at a Web site. This paper aims to classify user behavior in identifying the patterns of the browsing and navigation data of web users and also measure the performance of the Frequent Pattern (FP) Growth algorithm and Apriori algorithm...
This paper presents the formalized apparatus of mining inter-dimensional association rules in multidimensional data, their representation by means of templates, and also the method of all frequent item sets generation in multidimensional data with help of which association rules can be generated by preset templates.
Frequent itemset mining is the first step of association rule mining. Association rule mining in online data mining is one of the most chalanging task due to data stream. A data stream is a huge, infinite, continuous, fast changing and rapid sequence of data elements. Traditional techniques for finding frequent itemset required many passes but stream data require only one scan over the data for finding...
Techniques for efficient mining of frequent patterns have been studied extensively by many researchers. However, the previously proposed techniques still encounter some performance bottlenecks when mining databases with different data characteristics such as, dense vs. sparse, long vs. short patterns, memory-based vs. disk-based, etc. In this study, explored the unifying feature among the internal...
Sequential pattern mining is valuable approach to uncover consumer buying behaviour from huge sequence database. Weather prediction, web log analysis, stock market analysis, scientific research, sales analysis, and so on are the application of sequential pattern mining. The pattern that is recent and profitable can't discover by conventional sequential pattern mining. So, RFM-based sequential pattern...
This paper proposes an approach of the spatio-temporal data mining in order to predict next learning steps (next ubiquitous learning logs to be learned) in accordance with their situations or context from past learners' experiences in their daily lives accumulated in the ubiquitous learning system called SCROLL (System for Capturing and Reminding of Learning Log). Ubiquitous learning log (ULL) is...
Association Rules is one of the data mining techniques which is used for identifying the relation between one item to another. Creating the rule to generate the new knowledge is a must to determine the frequency of the appearance of the data on the item set so that it is easier to recognize the value of the percentage from each of the datum by using certain algorithms, for example apriori. This research...
The field of privacy pursues rapid advances in recent years because of the increases in the ability to store data. One of the most important topics in research community is Privacy preserving data mining (PPDM). Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. People today have become well aware of the privacy...
Rare association rule mining provides useful information from large database. Traditional association mining techniques generate frequent rules based on frequent itemsets with reference to user defined: minimum support threshold and minimum confidence threshold. It is known as support-confidence framework. As many of generated rules are of no use, further analysis is essential to find interesting...
The Aim of Association Rule Mining(ARM) is to find Frequent itemsets. Apriori Algorithm is one of the most efficient Frequent itemset mining Algorithm. However Frequent itemset mining does not includes interestingness or utility. Utility mining is a new area in data mining which considers all external utility factors. A specialized form of Association Rule Mining is utility-frequent itemset mining,...
Frequent pattern mining has attracted a lot of attention in past twenty years because of its wide applications like commercial promotion, web search engines, and so forth. However, the execution performance suffers from the rapid growth on the database. Many of the past studies tried to use distributed computing technology to speed up the mining process, but few of them discussed how the appropriate...
This paper addresses the problem of association rules mining with large data sets using bees behaviors. The bees swarm optimization method have been successfully applied on small and medium data size. Nevertheless, when dealing Webdocs benchmark (the largest benchmark on the web), it is bluntly blocked after more than 15 days. Additionally, Graphic processor Units are massively threaded providing...
The pattern growth approach of association rule mining is very efficient as avoiding the candidate generation step which is utilized in Apriori algorithm. This research is about the revisiting the pattern growth approaches to discover the different research works carried out to improve the performance using different criteria like header table dealing, item search order, conditional database representation,...
In this paper, a pattern trend-based data mining approach has been proposed which convert the numeric stock data to symbolic notations, carries out association analysis through comparative study of apriori and proposed modified reverse apriori concepts and further applies the mined rules in predicting the movement of prices. Application of modified reverse apriori has shown drastic reduction in the...
One of the most important problems in data mining is frequent itemset mining. It requires very large computation and I/O traffic capacity. For that reason several parallel and distributed mining algorithms were developed. Recently the mapreduce distributed data processing paradigm is unavoidable and porting the current algorithms to mapreduce is in focus. In this paper a substantial frequent itemset...
The enormous amount of internet causes huge amount of data to be stored and processed which contains structured and semi structured formats. Due to extreme size of semi structured document, results of a particular query may be vast which leads to retrieving interpretable knowledge a tedious process. The query-answering system makes it achievable to make queries and retrieve results for XML documents...
Number of people who uses internet and websites for various purposes is increasing at an astonishing rate. More and more people rely on online sites for purchasing rented movies, songs, apparels, books etc. The competition between numbers of sites forced the web site owners to provide personalized services to their customers. So the recommender systems came into existence. Recommender systems are...
Association rules mining is a process of finding patterns from a very large volumes of data. The Apriori algorithm is the best-known association rules mining algorithm, whose objective is to find all co-occurrence relationships between data items. In this paper, a methodology that combines the Apriori algorithm with a domain-specific ontology is proposed. This method is to effectively utilize domain...
Concept map model has been widely used in e-learning for various applications. However, in the past researches, there are few attentions paid on constructing the personal concept map for diagnosing learner's learning status. Actually, it is difficult to construct the individual concept maps to reflect the real knowledge structure by learners themselves. To cope with this problem, in our previous study,...
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