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High utility itemset mining (HUIM) is an important data-mining task. Most of existing algorithms for HUIM do not consider transaction addition and deletion. When a database is updated, they need to scan the whole database to rebuild their data structures. To deal with this problem, an efficient tree structure IHUP-Tree is proposed. IHUP-Tree can be adjusted efficiently when a transaction is added...
The purpose of this study is to propose an approach to recommend classification algorithms for real-world classification problems. First, the extension rhombus thinking mode is used to construct performance indicators of the classification algorithm. Second, an extension-based recommendation method about classification algorithm is proposed. Third, a recommendation system is designed to implement...
With large companies and corporations becoming increasingly responsible for data collection, in recent years, a growing number of scientists have proposed using a variety of algorithms and different theories to solve the database problem. Even though existing solutions are effective in many cases many, problems are left to solve during the integration of database. The entity resolution (ER) is a crucial...
Given a large data collection, entity resolution is to find the records referring to the same entity. A crucial step of entity resolution is to compute the similarity between records. Without careful design, sometimes it has to compare all characters in two records to get a small similarity value. In this paper, we propose a novel method based on waves of records, which is a sequence of frequencies...
In many real applications, graph data is subject to uncertainties due to incompleteness and imprecision of data. Mining such uncertain graph data is semantically different from and computationally more challenging than mining conventional exact graph data. This paper investigates the problem of mining uncertain graph data and especially focuses on mining frequent subgraph patterns on an uncertain...
A major challenge in frequent subgraph mining is the sheer size of its mining results. In many cases, allow minimum support may generate an explosive number of frequent subgraphs, which severely restricts the usage of frequent sub graph mining. In this paper, we study anew problem of mining frequent jump patterns from graph databases. Mining frequent jump patterns can dramatically reduce the number...
Several efficient frequent subgraph mining algorithms have been recently proposed. However, the number of frequent graph patterns generated by these graph mining algorithms may be too large to be effectively explored by users, especially when the support threshold is low. In this paper, we propose to summarize frequent graph patterns by a much smaller number of representative graph patterns. Several...
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