Mamta Madan

Social Network Wrappers (SNWs): An Approach used for Exploiting and Mining Social Media Platforms

paper tries to portrait outline study on the detailed approaches which are related to the working of a Social Media Networks Extraction System (SMNES) or the Social media (SM) platform with the perception of Social Network Wrappers (SNWs) and their issues like creation, perpetuation and support etc. In this paper we discuss in detail the obstacle related to the generation or creation of SNWs, initiation and support, and other important approaches. At last, we discuss the problem related to SNWs maintenance; propose our recommendation in adapting Social Network Wrappers fully automatically (SNWs). KeywordsMedia Networks (SMNs), Social Media Network Extraction System (SMNES), Social Network Wrappers (SNW)

An Analytical Implementation of CART Using RStudio for Churn Prediction

Data mining is a technique for finding new and undiscovered patterns, which help in predicting the future trends. Nowadays, it is being applied in all the fields, may it be, the field of medicines or credit cards or banking and insurance or telecommunications. Decision tree is a simple and popular technique of data mining (commonly employed for predictive analysis) which can be used to forecast the future trends. There are several algorithms for decision tree generation like ID3, C4.5, CART which can be applied with the help of different software tools like WEKA, Rapid Miner, R. This paper focuses on applying data mining in the field of telecommunications, to predict the churning behavior of the customers.

Analyzing Online Groups or the Communities in Social Media Networks by Algorithmic Approach

This paper focuses on communities or clusters which are the sets of nodes with lots of links within and very less to the outside of the network. The paper explains the concept of online generation communities and their framework in online social media networks (OSMNs), especially largest networking site, i.e., Facebook. There are many popular methods available for community identification like Walktrap, Nibble, Label Propagation Algorithm (LPA), Fast Community Network Algorithm (FCNA) which had been explored in the last decade. The community framework (CF) is the important and integral part of the OSMNs, but still we have to find the absolutely correct definition of the community in the real-world networks. In this paper, we try to give a correct definition of the community with its few important traits and from that we are able to recommend a different, simple and innovative framework (either flowchart or algorithm) which will resemble the real-world network. In our approach, we try to incorporate or consider those nodes which are overlapped in the community framework with the concept of shortest paths. We believe that our approach will be more favorable than other network methods which mostly generate the partitions.

Trends and Pattern Analysis in Social Networks

The focus in India has now changed to providing world class higher education to all students aiming to compete with the world. Keeping this in mind, higher education institutions (HEIs) must figure out a way to make the change in accordance with technological advancements in social networking to motivate students and encourage an intuitive learning environment in their campus. Using social networking in higher education (HE) is being creative, productive, cost–effective, and is exceptionally critical owing to the complex nature of serving the population of the online generation (Og). Utilizing the social networking environment for learning and teaching at HEIs could be a financially effective and productive way of speaking to and connecting with higher education online members, which include students, faculties, administrators, staff, management, etc. A few illustrations of the HEI becoming a “Social Institution” are strengthening the HEI’s “brand” or reputation, managing and building online communities (staff, students, parents/guardians, graduated class/alumni), and streamlining processes for better productivity at less expense. This chapter focuses on effective implementation of data analytics techniques on social media datasets for helping Indian HEIs to compete effectively in the global market.