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Association rule mining is a technique of generating frequent item sets so that the analysis on the basis of these sets can be used for different application areas such as analysis of network traffic. Although the frequent sets generated using apriori algorithm provides less computational time and provides less frequent sets, but the technique that we are implemented here provides less computational...
There are many applications of using association rules in data streams, such as market analysis, network security, sensor networks and web tracking. Mining closed frequent item sets is a further work of mining association rules, which aims to find the subsets of frequent item sets that could extract all frequent item sets. Formally, a closed frequent item set is a frequent item set which has no superset...
Most outlier detection algorithms are proposed to discover outlier patterns from static databases. Those algorithms are infeasible for instant identification of outlier patterns in data streams that continuously arriving and unbounded data serve as the data sources in many applications such as sensor data feeding. In this paper an association rules based method is proposed to find outlier patterns...
Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a...
Data mining deepens the data analysis, also is able to mine the interesting mode hiding in mass data automatically. As a new data analysis technology, data mining makes full use of the modern software technology and computer scientific knowledge, has the extremely important research significance, provides for researchers of various fields with a new intelligence means to realize and use data. In data...
Association Rules discovered by association rule mining may contain some sensitive rules, which may cause potential threats towards privacy and security. Many of the researchers in this area have recently made efforts to preserve privacy for sensitive association rules in statistical database. In this paper, we propose a heuristic based association rule hiding using oracle real application clusters...
This paper introduces the concept of data mining and its an important branch - association rules, describes the basic concept of association rules, the basic model of mining association rules, introduces the classical algorithm of association rules, and then classified discusses the association rules mining from several angles such as width, depth, partition, sampling and incremental updating. Finally,...
The generation of frequent itemsets is the key of association rules mining. Based on bit vectors and its intersection operation of the DLG ideas, this paper presents a new k-frequent itemsets generation algorithm based on bit matrix. The algorithm scans the database only once, using bit matrix of alternative association graph to store, constructing screening conditions to reduce the validation of...
There are many methods which have been developed for improving the time of mining frequent itemsets. However, the time for generating association rules were not put in deep research. In reality, if a database contains many frequent itemsets (from thousands up to millions), the time for generating association rules is more longer than the time for mining frequent itemsets. In this paper, we present...
The paper analyzes the basic ideas and the shortcomings of Apriori algorithm, studies the current major improvement strategies of it. In order to solve the low performance and efficiency of the algorithm caused by its generating lots of candidate sets and scanning the transaction database repeatedly, it studies the pruning optimization and transaction reduction strategies, and on this basis, the improved...
With the database technology, artificial intelligence and mathematical statistics the development of technology, database data mining technology arises at the historic moment. This paper presented related formal definitions of association rules and the basic algorithm for association rules mining in data streams. Based on systematic investigation of association rules mining researches on streams data,...
Association rule mining is a well researched method for discovering interesting relations between variables in large databases. The first phase of association rule mining is the discovery of frequent itemsets which is a critical step and plays important role in many data mining tasks. The existing algorithms for finding frequent itemsets suffer from many problems related to memory and computational...
A novel adaptive method included two phases for discovering maximal frequent itemsets is proposed. A flexible hybrid search method is given, which exploits key advantages of both the top-down strategy and the bottomup strategy. Information gathered in the bottom-up can be used to prune in the other top-down direction. Some efficient decomposition and pruning strategies are implied, which can reduce...
Association mining in finding relationships between items in a dataset has been demonstrated to be practical in business applications. Many companies are applying association mining on market data for analyzing consumers' purchase behavior. The Apriori algorithm is the most established algorithm for association mining in finding frequent itemsets. However, the time complexity of the Apriori algorithm...
We consider the problem of applying probability concepts to discover frequent itemsets in a transaction database. The paper presents a probabilistic algorithm to discover association rules. The proposed algorithm outperforms the a priori algorithm for larger databases without losing a single rule. It involves a single database scan and significantly reduces the number of unsuccessful candidate sets...
To reduce the number of candidate itemsets and the times of scanning database, and to fast generate candidate itemsets and compute support, this paper proposes an algorithm of association rules mining based on attribute vector, which is suitable for mining any frequent itemsets. The algorithm generates candidate itemsets by computing nonvoid proper subset of attributes items, it uses ascending value...
The Apriori algorithm is a most influential one to excavate association rules. The basic idea of the algorithm is: identify all the frequent itemsets to get association rule. This paper presents the improved Apriori algorithm based on interested items, which mainly construct an ordered interested table and traverse it to excavate frequent itemsets quickly. The paper also by writing c# code achieves...
Mining frequent itemsets is one of the most investigated fields in data mining. It is a fundamental and crucial task. Apriori is among the most popular algorithms used for the problem but support count is very time-consuming. In order to improve the efficiency of Apriori, a novel algorithm, named BitApriori, for mining frequent itemsets, is proposed. Firstly, the data structure binary string is employed...
Apriori algorithm is a classical mining algorithm uses the association rules. After analyzing the Apriori algorithm, this algorithm is inefficient due to it scans the database many times. Based on the strategy of accessing to database once, a new improved algorithm founded on the Apriori is put forward in this paper. Experiments show that it can significantly improve computation efficiency, i e reduce...
The most time consuming operation in Priori-like algorithms for association rule mining is the computation of the frequency of the occurrences of itemsets (called candidates) in the database. In this paper, a fast algorithm has been proposed for generating frequent itemsets without generating candidate itemsets and association rules with multiple consequents. The proposed algorithm uses Boolean vector...
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