Titlebook: Web-Age Information Management; 17th International C Bin Cui,Nan Zhang,Dexi Liu Conference proceedings 2016 Springer International Publishi

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Efficient Mining of Uncertain Data for High-Utility Itemsetsowever, uncertainty that are embedded in big data which collected from experimental measurements or noisy sensors in real-life applications. In this paper, an efficient algorithm, namely Mining Uncertain data for High-Utility Itemsets (MUHUI), is proposed to efficiently discover potential high-utili

Overstate

Efficient Mining of Uncertain Data for High-Utility Itemsetsowever, uncertainty that are embedded in big data which collected from experimental measurements or noisy sensors in real-life applications. In this paper, an efficient algorithm, namely Mining Uncertain data for High-Utility Itemsets (MUHUI), is proposed to efficiently discover potential high-utili

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四牛在彎曲

An Improved HMM Model for Sensing Data Predicting in WSNnsolved problems for WSN. Predicting methods for data recovery by empirical treatment, mostly based on statistics has been studied exclusively. Machine learning models can greatly enhance the predicting performance. In this paper, an improved HMM is proposed for multi-step predicting of wireless sen

空氣傳播

Barrister

eXtreme Gradient Boosting for Identifying Individual Users Across Different Digital Devicesugh different electronic devices. Identifying individual users across different digital devices is now becoming a hot research topic. Methods based on name, email and other demographic information have received much attention. However, it is often difficult to obtain a complete set of information. I

是他笨

蛙鳴聲

Two-Phase Mining for Frequent Closed Episodessode mining strategies have been suggested, which can be roughly classified into two classes: Apriori-based breadth-first algorithms and projection-based depth-first algorithms. As we know, both kinds of algorithms are level-wise pattern growth methods, so that they have higher computational overhea

archetype

Effectively Updating High Utility Co-location Patterns in Evolving Spatial Databasesborhood. In spatial high utility co-location mining, we should consider the utility as a measure of interests, by considering the different value of individual instance that belongs to different feature. This paper presents a problem of updating high utility co-locations on evolving spatial database

pericardium

Effectively Updating High Utility Co-location Patterns in Evolving Spatial Databasesborhood. In spatial high utility co-location mining, we should consider the utility as a measure of interests, by considering the different value of individual instance that belongs to different feature. This paper presents a problem of updating high utility co-locations on evolving spatial database