Temporal Data Mining via Unsupervised Ensemble Learning.pdf

Temporal Data Mining via Unsupervised Ensemble Learning PDF

Yun Yang

Temporal Data Mining via Unsupervised Ensemble Learning Temporal Data Mining via Unsupervised Ensemble Learning not only provides an overview of temporal data mining and an in-depth knowledge of temporal data clustering and ensemble learning techniques but also provides a rich blend of theory and practice with three proposed novel approaches. Since each temporal clustering approach favors differently structured temporal data or types of temporal data with certain assumptions, and since there is nothing universal that can solve all problems, this book enables practitioners to understand the characteristics of both clustering algorithms and the target temporal data so as to select the right approach to successfully solve each different situation. Key Features : The first novel approach is based on the ensemble of Hidden Markov Model-based partitioning clustering, associated with a hierarchical clustering refinement, to solve problems by finding the intrinsic number of clusters and model initialization problems which exist in most model-based clustering algorithms

19/03/2020 · The increased availability of online reviews requires a relevant solution to draw chronological insights from review streams. This paper introduces temporal sentiment analysis by adopting the automatic contextual analysis and ensemble clustering (ACAEC) algorithm. ACAEC is a clustering algorithm which utilizes contextual analysis and a clustering ensemble learning. Machine learning, data mining, temporal data clustering, and ensemble learning are very popular in the research field of computer science and relevant subjects.

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9780128116548 ISBN
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Temporal Data Mining via Unsupervised Ensemble Learning.pdf


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Notes actuelles

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A survey on ensemble learning - hep.com.cn 16/10/2019 · Ensemble learning, as one research hot spot, aims to integrate data fusion, data modeling, and data mining into a unified framework. Specifically, ensemble learning firstly extracts a set of features with a variety of transformations. Based on these learned features, multiple learning algorithms are utilized to produce weak predictive results. Finally, ensemble learning fuses the informative

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Bi-weighted ensemble via HMM-based approaches … To improve the performance of ensemble techniques for temporal data clustering, we propose a novel bi-weighted ensemble in this paper to solve the initialization and automated model selection problems encountered by all HMM-based clustering techniques and their applications. Our proposed ensemble features in a bi-weighting scheme in the process of examining each partition and optimizing

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Temporal Data Mining via Unsupervised Ensemble … Temporal Data Mining via Unsupervised Ensemble Learning by Yun Yang and Publisher Elsevier (S&T). Save up to 80% by choosing the eTextbook option for ISBN: 9780128118412, 0128118415. The print version of this textbook is ISBN: 9780128116548, 0128116544.