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Data Mining Prospects in Mobile Social Networks

Data Mining Prospects in Mobile Social Networks

Gebeyehu Belay Gebremeskel, Zhongshi He, Huazheng Zhu
Copyright: © 2013 |Pages: 28
ISBN13: 9781466642133|ISBN10: 1466642130|EISBN13: 9781466642140
DOI: 10.4018/978-1-4666-4213-3.ch008
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MLA

Gebremeskel, Gebeyehu Belay, et al. "Data Mining Prospects in Mobile Social Networks." Data Mining in Dynamic Social Networks and Fuzzy Systems, edited by Vishal Bhatnagar, IGI Global, 2013, pp. 145-172. https://doi.org/10.4018/978-1-4666-4213-3.ch008

APA

Gebremeskel, G. B., He, Z., & Zhu, H. (2013). Data Mining Prospects in Mobile Social Networks. In V. Bhatnagar (Ed.), Data Mining in Dynamic Social Networks and Fuzzy Systems (pp. 145-172). IGI Global. https://doi.org/10.4018/978-1-4666-4213-3.ch008

Chicago

Gebremeskel, Gebeyehu Belay, Zhongshi He, and Huazheng Zhu. "Data Mining Prospects in Mobile Social Networks." In Data Mining in Dynamic Social Networks and Fuzzy Systems, edited by Vishal Bhatnagar, 145-172. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-4213-3.ch008

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Abstract

Unable to accommodating new technologies, including social technology, mobile devices and computing are other potential problems, which are significant challenges to social-networking service. The very broad range of such social-networking challenges and problems are demanding advanced and dynamic tools. Therefore, in this chapter, we introduced and discussed data mining prospects to overcome the traditional social-networking challenges and problems, which led to optimization of MSNs application and performances. The proposed method infers defining and investigating social-networking problems using data mining techniques and algorithms based on the large-scale data. The approach is also exploring the possible potential of users and systems contexts, which leads to mine the personal contexts such as the users’ locations and situations from the mobile logs. In these sections, we discussed and introduced new ideas on social technologies, data mining techniques and algorithm’s prospects, social technology’s key functional and performances, which include social analysis, security and fraud detections by presenting a brief analysis, and modeling based descriptions. The approach also empirically discussed using the real survey data, which the result showed how data mining vitally significant to explore MSNs performance and its crosscutting impacts. Finally, this chapter provides fundamental insight to researchers and practitioners who need to know data mining prospects and techniques to analyze large, complex and frequently changing data. This chapter is also providing a state-of-the-art of data mining techniques and algorithm’s dynamic prospects.

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