Multi-Source Data Sensing in Mobile Personalized Healthcare Systems: Semantic Linking and Data Mining

This chapter introduces the concept of learning style and Memletics learning style inventory, and uses open-source data mining software WEKA to cluster the students of experiment classes in four high schools according to the values of seven dimensions in the Memletics learning style inventory that are calculated based on the survey result about their learning styles. The clustering result demonstrates that verbal and physical are always positively associated with exam scores, visual dimension usually has negative association with score exams; the association of learning style with exam scores remains almost static, and the high, medium, and low sum of dimension values of learning style corresponds to high schools in developed, developing, and undeveloped area in China, respectively. The findings are analyzed. The implication of learning style for intelligent instruction of English subject as a foreign language is suggested.

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Application of Open Source Data Mining Software Weka in Marketing Teaching

10.1007/978-3-030-87903-7_44 ◽

2021 ◽

pp. 356-365

Author(s):

Zhengteng Hao

Keyword(s):

Data Mining ◽

Open Source ◽

Open Source Data ◽

Source Data

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Energy efficient key agreement scheme for ubiquitous and continuous remote healthcare systems using data mining technique

Cluster Computing ◽

10.1007/s10586-017-0903-7 ◽

2017 ◽

Vol 21 (1) ◽

pp. 469-480 ◽

Cited By ~ 3

Author(s):

Saleh M. Al-Saleem ◽

Aftab Ali ◽

Naveed Khan

Keyword(s):

Data Mining ◽

Energy Efficient ◽

Key Agreement ◽

Healthcare Systems ◽

Data Mining Technique ◽

Mining Technique ◽

Remote Healthcare ◽

Using Data

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Arithmetic Research on Data Mining Technology and Associative Rules Mining

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.3949 ◽

2014 ◽

Vol 556-562 ◽

pp. 3949-3951

Author(s):

Jian Xin Zhu

Keyword(s):

Data Mining ◽

High Performance ◽

Knowledge Engineering ◽

Business Decisions ◽

Depth Data ◽

Source Data ◽

Wide Availability ◽

High Level ◽

Performance Computing ◽

Level Analysis

Data mining is a technique that aims to analyze and understand large source data reveal knowledge hidden in the data. It has been viewed as an important evolution in information processing. Why there have been more attentions to it from researchers or businessmen is due to the wide availability of huge amounts of data and imminent needs for turning such data into valuable information. During the past decade or over, the concepts and techniques on data mining have been presented, and some of them have been discussed in higher levels for the last few years. Data mining involves an integration of techniques from database, artificial intelligence, machine learning, statistics, knowledge engineering, object-oriented method, information retrieval, high-performance computing and visualization. Essentially, data mining is high-level analysis technology and it has a strong purpose for business profiting. Unlike OLTP applications, data mining should provide in-depth data analysis and the supports for business decisions.

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Information Visualization with Text Data Mining for Knowledge Discovery Tools in Bioinformatics

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.277-279.259 ◽

2005 ◽

Vol 277-279 ◽

pp. 259-265

Author(s):

Jin Ah Park ◽

Chang Su Lee ◽

Jong C. Park

Keyword(s):

Data Mining ◽

3D Visualization ◽

Three Dimensional ◽

Mitogen Activated Protein Kinase ◽

Text Data ◽

Text Data Mining ◽

Visualization System ◽

Source Data ◽

Mitogen Activated Protein ◽

Available Information

An abundant amount of information is produced in the digital domain, and an effective information extraction (IE) system is required to surf through this sea of information. In this paper, we show that an interactive visualization system works effectively to complement an IE system. In particular, three-dimensional (3D) visualization can turn a data-centric system into a user-centric one by facilitating the human visual system as a powerful pattern recognizer to become a part of the IE cycle. Because information as data is multidimensional in nature, 2D visualization has been the preferred mode. However, we argue that the extra dimension available for us in a 3D mode provides a valuable space where we can pack an orthogonal aspect of the available information. As for candidates of this orthogonal information, we have considered the following two aspects: 1) abstraction of the unstructured source data, and 2) the history line of the discovery process. We have applied our proposal to text data mining in bioinformatics. Through case studies of data mining for molecular interaction in the yeast and mitogen-activated protein kinase pathways, we demonstrate the possibility of interpreting the extracted results with a 3D visualization system.

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