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Frequent subgraph mining from streams of linked graph structured data
(CEUR Workshop Proceedings, 2015)
Nowadays, high volumes of high-value data (e.g., semantic web data) can be generated and published at a high velocity. A collection of these data can be viewed as a big, interlinked, dynamic graph structure of linked ...
Effectively and efficiently mining frequent patterns from dense graph streams on disk
(Elsevier, 2014)
In this paper, we focus on dense graph streams, which can be generated in various applications ranging from sensor networks to social networks, from bio-informatics to chemical informatics. We also investigate the problem ...
Tightening upper bounds to the expected support for uncertain frequent pattern mining
(Elsevier, 2014)
Due to advances in technology, high volumes of valuable data can be collected and transmitted at high velocity in various scientific and engineering applications. Consequently, efficient data mining algorithms are in demand ...
Mining of diverse social entities from linked data
(CEUR Workshop Proceedings, 2014)
Nowadays, high volumes of valuable data can be easily generated or collected from various data sources at high velocity. As these data are often related or linked, they form a web of linked data. Examples include semantic ...
Frequent pattern mining from dense graph streams
(CEUR Workshop Proceedings, 2014)
As technology advances, streams of data can be produced in many applications such as social networks, sensor networks, bioinformatics, and chemical informatics. These kinds of streaming data share a property in common--namely, ...
Sports data mining: predicting results for the college football games
(Elsevier, 2014)
In many real-life sports games, spectators are interested in predicting the outcomes and watching the games to verify their predictions. Traditional approaches include subjective prediction, objective prediction, and simple ...
A tree-based algorithm for mining diverse social entities
(Elsevier, 2014)
DiSE-growth, a tree-based (pattern-growth) algorithm for mining DIverse Social Entities, is proposed and experimentally assessed in this paper. The algorithm makes use of a specialized data structure, called DiSE-tree, for ...
Frequent itemset mining of distributed uncertain data under user-defined constraints
(Edizioni Libreria Progetto, 2012)
Many existing distributed data mining algorithms do not allow users to express the patterns to be mined according to their intention via the use of constraints. Consequently, these unconstrained mining algorithms can yield ...
Mining frequent patterns from precise and uncertain data
(UNIFACS, 2011-01)
Data mining has gained popularity over the past two decades and has been considered one of the most prominent areas of current database research. Common data mining tasks include finding frequent patterns, clustering and ...
Game data mining: clustering and visualization of online game data in cyber-physical worlds
(Elsevier, 2017)
Since its debut in May 2016, Overwatch has quickly become a popular team-based online video game. Despite the popularity of Overwatch, many new players---who join the game unsure how to compete with the game’s veterans---feel ...