Alhajj, RedaSarraf Shirazi, Atieh2017-12-182017-12-182012Sarraf Shirazi, A. (2012). Data driven network construction and analysis extending the functionality of netdriller (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/5027http://hdl.handle.net/1880/106028Bibliography: p. 78-84A few pages are in colour.Social network analysis has emerged as a technique in sociology. However, it has become more and more interesting to researchers of other fields. The flexibility and scalability supported by the new technology encouraged the extension of the social network technology to handle new applications. A social network is defined as a set of nodes and a set of links connecting them. Social network analysis is the task of analyzing a social network with the purpose of gaining some information about the network such as patterns of connection or important nodes. However, there are a lot of applications where only raw data is available. Usually, the data sets contain data objects with their set of features. In this work, we propose an approach to construct a social network fom a raw data set. The approach is based on the assumption that if two objects are similar, there is a higher probability that they are placed in the same cluster in different clustering solutions. Based on this assumption, we use a multi-objective genetic algorithm approach to find different solutions for partitioning the data objects. The actors of the final social network are the data objects, and the link between them shows the ratio of partitioning solutions that the objects are placed in the same cluster. This work is implemented in NetDriller, a powerful social network analysis tool developed at Data Mining group at the University of Calgary. We show the validity of our approach by evaluating both the intermediate clustering results and the constructed social network in a case study on stock market.vi, 84 leaves : ill. ; 30 cm.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.master thesis10.11575/PRISM/5027