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An Adaptive Approach to Learning the Preferences of Users in a Social Network Using Weak
"... Abstract—Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional esti ..."
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Abstract—Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary; estimating a user's interests typically involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked imposes stringent constraints on the "unlearning ” capabilities of the estimator used. Therefore, resorting to strong estimators that converge with a probability of 1 is inefficient since they rely on the assumption that the distribution of the user's preferences is stationary. In this vein, we propose to use a family of stochastic-learning based Weak estimators for learning and tracking a user's time varying interests. Experimental results demonstrate that our proposed paradigm outperforms some of the traditional legacy approaches that represent the state-of-the-art technology.
Utility Fp-Tree: An Efficient Approach for Mining of Weighted Utility Itemsets
"... Abstract:- Conventional association rules mining cannot satisfy the demands emerging from certain real applications. By regarding the diverse values of distinct items as utilities, utility mining concentrates on discovering the itemsets with high utilities. In recent times, high utility pattern mini ..."
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Abstract:- Conventional association rules mining cannot satisfy the demands emerging from certain real applications. By regarding the diverse values of distinct items as utilities, utility mining concentrates on discovering the itemsets with high utilities. In recent times, high utility pattern mining is one of the most significant research issues in data mining because of its ability to account for the non-binary frequency values of items in transactions and diverse profit values of each item. In this paper, we have presented an efficient tree structure for mining of high utility itemsets. At first, we have developed a novel utility frequent-pattern tree structure, an extended tree structure for storing crucial information about utility itemsets. Then, we have utilized the pattern growth methodology for mining the complete set of utility patterns. The efficiency of the high utility itemsets mining is achieved with two major concepts: 1) a large database is compressed into a smaller data structure as well as the utility FP-tree avoids repeated database scans, 2) our proposed FP-tree-based utility mining utilize the pattern growth method to avoid the costly generation of a large number of candidate sets in which it dramatically reduces the search space. Experimental analysis is carried out on our mining trees structure concept using different real life datasets. The performance evaluation of our proposed approach is efficient in mining high utility itemsets.
Bit Mask Search Algorithm for Trajectory Database Mining P.Geetha
"... Mining great service entities in trajectory database indicates to the exposure of entities with huge service like acquisition. The extensive number of contender entities degrades the mining achievement in terms of execution time and space stipulation. The position may become worse when the database ..."
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Mining great service entities in trajectory database indicates to the exposure of entities with huge service like acquisition. The extensive number of contender entities degrades the mining achievement in terms of execution time and space stipulation. The position may become worse when the database consists of endless lengthy transactions or lengthy huge utility entity sets. In this paper, we use two algorithms, namely Utility Pattern Growth (UP –Growth) for mining huge utility entities with a set of adequate approaches for pruning contender entities. The previous algorithms do not contribute any compaction or compression mechanism the density in bit vector regions. To raise the density in bit-vector sector the Bit search Mask Search (BM Search) starts with an array list. From root node, a BM Search representation for each frequent pattern is designed which gives an acceptable compression and compaction in bit search measure than UP Growth algorithm. This paper compared two algorithms such as UP Growth and BM Search. In the analysis of two algorithms BM Search produces best result compared than the other algorithms. An experimental result shows the comparison of two algorithms.
A NEW DATA STREAM MINING ALGORITHM FOR INTERESTINGNESS-RICH ASSOCIATION RULES
"... Frequent itemset mining and association rule generation is a challenging task in data stream. Even though, various algor-ithms have been proposed to solve the issue, it has been found out that only frequency does not decides the significance interestingness of the mined itemset and hence the associa ..."
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Frequent itemset mining and association rule generation is a challenging task in data stream. Even though, various algor-ithms have been proposed to solve the issue, it has been found out that only frequency does not decides the significance interestingness of the mined itemset and hence the association rules. This accelerates the algorithms to mine the association rules based on utility i.e. proficiency of the mined rules. However, fewer algorithms exist in the literature to deal with the utility as most of them deals with reducing the complexity in frequent itemset/association rules mining algorithm. Also, those few algorithms consider only the overall utility of the association rules and not the consistency of the rules throughout a defined number of periods. To solve this issue, in this paper, an enhanced association rule mining algorithm is proposed. The algorithm introduces new weightage validation in the conventional association rule mining algorithms to validate the utility and its consistency in the mined association rules. The utility is validated by the integrated calculation of the cost/price efficiency of the itemsets and its frequency. The consistency validation is performed at every defined number of windows using the probability distribution function, assuming that the weights are normally distributed. Hence, validated and the obtained rules are frequent and utility efficient and their interestingness are distributed throughout the entire time period. The algorithm is implemented and the resultant rules are compared against the rules that can be obtained from conventional mining algorithms.
