Gangin Lee

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Frequent graph pattern mining is one of the most interesting areas in data mining, and many researchers have developed a variety of approaches by suggesting efficient, useful mining techniques by integration of fundamental graph mining with other advanced mining works. However, previous graph mining approaches have faced fatal problems that cannot consider(More)
Frequent itemset mining allows us to find hidden, important information from large databases. Moreover, processing incremental databases in the itemset mining area has become more essential because a huge amount of data has been accumulated continually in a variety of application fields and users want to obtain mining results from such incremental data in(More)
In recent years, with the increasing number of blogs to share information, the ratio of blogs on the World Wide Web has been raised. In this regard, the problem of information quality has come up due to the rapidly increasing amount of information in a blogosphere. Therefore, discovering good quality information is one of the significant issues in the blog(More)
As one of the variations in frequent pattern mining, erasable pattern mining discovers patterns with benefits lower than or equal to a user-specified threshold from a product database. Although traditional erasable pattern mining algorithms can perform their own mining operations on static mining environments, they are not suitable for dealing with dynamic(More)
Frequent graph mining has been proposed to find interesting patterns (i.e., frequent sub-graphs) from databases composed of graph transaction data, which can effectively express complex and large data in the real world. In addition, various applications for graph mining have been suggested. Traditional graph pattern mining methods use a single minimum(More)