Data Mining and Social Network Analysis

This document discusses the use of data mining in social network analysis, including classification, association, clustering, and change detection. It also explores the future recommendations and the importance of data mining in understanding social behavior and dynamics.

Data Mining and Social Network Analysis 1
DATA MINING AND SOCIAL NETWORK ANALYSIS: A STUDY OF CURRENT USE OF
DATA MINING IN SOCIAL NETWORK ANALYSIS
By (Name of Student)
(Institutional Affiliation)
(Date of Submission)

Data Mining and Social Network Analysis 2
Introduction
Data mining in business analytics refers to the computational process of discovering
patterns, trends, and behaviors in large datasets using machine learning techniques, artificial
intelligence, database systems, and statistics. Social networks, on the other hand, refer to the
representation of the data by means that are easy and simple to execute (Fan and Buffet 2013). Data
mining and social network analysis are essential steps in predictive analysis process. The
information overload in an organization originating from the growing quantity of the “Big data”
during the past year requires the introduction and integration of the new processing approaches into
every activity and object (Romero and Ventura, 2013).
Handling big amounts of data manually is rigorous and sometimes hectic for any person
involved in the analysis of the business data at large. Several methods and ways have been invented
to solve this problem such as data mining and social networking. In regard to these, business and
organizations have been deploying sophisticated data mining techniques and social networking in
business to evaluate the rich data source, identify the pattern, and exploit this information for
various decision making (Dawson et al., 2014)
Data mining has helped in field of social networks by providing proficient means and ways
for the execution and better usage of the database. Data mining has helped in the field of social
networks by achieving the following objectives;
i. Classification of the data; in this process, the given data is classified into different
categories for identification and other references.
ii. Association; in this it, data mining helps in discovering the relationship between various
databases and the relationship between the attributes of single database.

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iii. Clustering of the data; this entails categorizing and grouping of the data into other new
classes such that it helps in describing data.
iv. Detection of the change; in this method, significant changes in the data are identified
from the previous measured values.
Furthermore, due to the rising of social networks, there exists strong consequences to the set of
techniques developed for mining graphs and thus data mining is considerable (Lee and Yang 2014).
Social networks are rooted in many sources of data and at many different scales.
Future recommendations
By considering the existence and applications of data mining in social networks, it would be
recommendable for any business or organization to consider data mining techniques for the
improvement of duties. Social network will become more important in years to come since many
people are likely to communicate and interact with one another on webs (Dawson eta al, 2014). Use
of data mining in social network will not only keep the key to understanding the social behavior
and the dynamics of the groups on a large scale but also important in developing new equipment
and functions to support communications in social networks. Social networks are rooted in many
sources of data and at many different scales (Lee and Yang, 2014).
Conclusion
The rise of data mining in social network analysis provides concrete values to the set
techniques that are developed for mining social networks as well as graphing. Data mining on social
network thus provides proficient way to execute and make use of database for users of social
networks. Moreover, network analysis has become a very unique and popular area of research as it
is very important for many applications.

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References
Dawson, S., Gašević, D., Siemens, G. and Joksimovic, S., (2014). Current state and future trends: A
citation network analysis of the learning analytics field. In Proceedings of the fourth international
conference on learning analytics and knowledge (pp. 231-240). ACM.
Fan, W., and Bifet, A., (2013). Mining big data: current status, and forecast to the future. ACM
sIGKDD Explorations Newsletter, 14(2), pp.1-5.
Lee, J., Kao, H.A. and Yang, S., (2014). Service innovation and smart analytics for industry 4.0 and
a big data environment. Procedia Carp, 16, pp.3-8.
Romero, C. and Ventura, S., (2013). Data mining in education. Wiley Interdisciplinary Reviews:
Data Mining and Knowledge Discovery, 3(1), pp.12-27.