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With the rising popularity of smartphones and the rapid growth of mobile applications, understanding the app usage behavior of mobile users is of growing importance for both app designers and service providers. Different from previous studies mining the correlation between apps and physical world factors, e.g. location, time, etc., in this paper we focus on the interdependency among apps and try to...
In this paper, we use apriori algorithm to find the influence factors of the satisfaction with compulsory education funds. After the rules generated, we eliminate the rules with many antecedent items if their values of Confidence aren't significantly higher than those of the rules with less antecedent items. According to the properties of some commonly objective interestingness measures and the result...
In recent years, not only automotive market of China is booming, but also a great progress is being made in automotive loan business. Automotive credit risk prevention and credit ratings have become the focus of attention gradually. However, there is few literature published on automotive loan by data mining methods currently. Association rule learning is a popular and well researched method for discovering...
Weighted item-set mining is used to find the profitable connection between the data. There are two types of items contained in dataset i.e. frequent and infrequent. Infrequent item-sets are nothing but items which are rarely found in database. Mining frequent items in data mining are very helpful for retrieving the related data present in the dataset. Using transactional dataset as an input dataset...
Data Mining is a field of computer science that is concerned with extracting useful information from varied sources. In an era where information has become the inherent necessity of human beings, its increased relevance and usefulness has taken focus as need of the hour. The most important part of this association rule mining is the mining of item sets that are frequent. Market basket analysis is...
The problem of generating large frequent itemset for the generation of association rules in the transactional database is considered. Previous work in this field already proposes many algorithms like Apriori, FP-growth, and their variations. Reverse-Apriori which is also a variation of Apriori for finding large frequent itemset in reverse manner, it has its own advantages and limitations like Apriori...
Traditional data mining approaches offers simply statistical analysis with discovery of hidden knowledge and frequent patterns. It succeeded in finding the correlation among items by statistical significance but could not provide additional parameter to knowledge discovery. In contrast to traditional approach, use of profit significance as a measure to calculate new support and confidence established...
Association rule mining is a standard technique used for finding the relationships among the itemsets in a database. The method of extracting the frequent itemsets from the database using existing algorithms has several disadvantages such as generation of large number of candidate itemsets, increase in computational time and database scan. With this aim, the paper proposes Mining Interesting Itemsets...
Association rule mining is one of the most important data mining techniques. Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items (i.e. absent from transactions). Negative association rules are useful in market-basket analysis...
The discovery of association rules is one of the very important tasks in data mining. Association rules help in the generation of more general and qualitative knowledge which in turn helps in decision making. Association rules deal with transactions of both binary values and quantitative data.[9} The traditional algorithms for mining association rules are built on binary attributes databases, which...
Association rule mining is an important data analysis method for discovering associations within data. Recently, some researchers have extended association rule mining techniques to imprecise or uncertain data. However, the question arises as to how we can mine relevant and interesting patterns from uncertain data. Additionally, using the Σ-count, the summation of a large number of itemsets with very...
A large percentage of computing capacity in today's large high-performance computing systems is wasted because of failures. Consequently current research is focusing on providing fault tolerance strategies that aim to minimize fault's effects on applications. By far the most popular technique is the checkpointrestart strategy. A complement to this classical approach is failure avoidance, by which...
Association rule is an important model in data mining. However, traditional association rules are mostly based on the support and confidence metrics, and most algorithms and researches assumed that each attribute in the database is equal. In fact, because the user preference to the item is different, the mining rules using the existing algorithms are not always appropriate to users. By introducing...
Association rule mining aims at generating association rules between sets of items in a database. Now a day, due to huge accumulation in the database technology, the data are representing in the high dimensional data space. However, it is becoming very tedious to generate association rules from high dimensional data, because it contains different dimensions or attributes in the large data bases. In...
Spatial autocorrelation is a very general statistical property of economic variables, it indicates correlation of a variable with itself through space. Spatial association rule mining, discovery of interesting, meaningful rules in spatial databases, ignores autocorrelation of the spatial data, or just generalizes the spatial data into attribute data currently. In order to compare the results between...
Low support makes dramatic increase in the number of itemsets and brings less efficient frequent itemset mining. Correlation measures introduced to restrict the number of frequent itemsets generated in order to improve the efficiency of mining under certain conditions. An improved FP-Tree algorithm using node linked list FP-Tree is proposed. This algorithm exploits efficient pruning strategies using...
Mining of association rules has become an important area in the research on data mining. However the traditional approaches based on support-confidence framework maybe generate a great number of redundant and wrong association rules. In order to solve the problems, a correlation measure is defined and added to the mining algorithm for association rules. According to the value of correlation measure,...
In mining association rules, Item sets with high-length usually has lower support, but still have potential value. To mine efficacious association rules under long-pattern, a new mining method of efficacious association rules is proposed under length-decreasing support constraint. Compare to other mining methods of association rules, the new method can mine more efficacious long-patterns and improve...
According to the existing mining algorithm of fuzzy association rules, a novel fuzzy positive and negative association rules algorithm will be proposed in this paper. We focus on the membership function of fuzzy set and minimum support parameters of positive and negative association rules and adopt a method that selects parameters automatically which is based on the k-means clustering. Besides, multi-level...
In data processing of the supermarket, people often use the Apriori algorithm to analyze the customer “shopping basket” Due to the large computation, Apriori algorithm has controlled the number of frequent item sets by using the minimum supporting threshold and pruning techniques, but meaningless frequent item sets still possibly exist. Divide goods into several broad categories and set up the weighted...
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