Description
The field of data mining is receiving significant attention in today's information-rich society, where data is available from different sources and formats, in large volumes, and no longer constitutes a bottleneck for knowledge acquisition. This rich information has paved the way for novel areas of research, particularly in the crime data analysis realm.
Data Mining Trends and Applications in Criminal Science and Investigations presents scientific concepts and frameworks of data mining and analytics implementation and uses across various domains, such as public safety, criminal investigations, intrusion detection, crime scene analysis, and suspect modeling. Exploring the diverse ways that data is revolutionizing the field of criminal science, this publication meets the research needs of law enforcement professionals, data analysts, investigators, researchers, and graduate-level students.
Reviews and Testimonials
Editors Isafiade and Bagula present readers with a collection of professional and research perspectives on contemporary data mining trends and their realized and potential applications in a variety of criminal science and investigative settings. The editors have organized the ten selections that make up the main body of the text into four parts devoted to challenges and existing strategies in public safety and crime mining; hotspot, spatial, and visual analytics; forensics, suspect modeling, and intelligence gathering; and denial of service, cyber-crime, and intrusion detection management.
– ProtoView Reviews
Author's/Editor's Biography
Omowunmi Isafiade (Ed.)
Omowunmi Isafiade is currently a senior teaching assistant and a doctoral researcher at the University of Cape Town (UCT), South Africa. She obtained an M.Sc. degree in Computer Science from her current affiliation (UCT), in December 2012. Past affiliations were with the African Institute for Mathematical Sciences (AIMS) and the University of the Western Cape, Cape Town, South Africa, where she obtained a post-graduate diploma (PGDs) degree in Mathematical Sciences in June 2010, and a Bachelor (BSc.) honors degree in computer science from Federal University of Agriculture Abeokuta (FUNAAB), Nigeria. Omowunmi's area of research includes Machine learning, data mining & knowledge management, ubiquitous intelligence and pattern recognition. Omowunmi has quality journal publications, technical reports and refereed conference publications in the aforementioned research areas.
Antoine Bagula (Ed.)
Antoine Bagula obtained his doctoral degree from the KTH-Royal Institute of Technology in Sweden. He held lecturing positions at Stellenbosch University (SUN) and the University of Cape Town (UCT) before joining the Computer Science department of the University of the Western Cape (UWC) in January 2014. Since 20016, Professor Bagula has consulted for the UNESCO, the World Bank and other international organizations on different telecommunication projects. Professor Bagula has been on the technical programme committees of more than 50 international conferences and on the editorial board of international journals. He has also co-chaired international conferences in the field of telecommunications and ICT. Professor Bagula has authored/co-authored more than 100 papers in peer-reviewed conferences, journals and book chapters. Professor Bagula's research interest is communication networking with a specific focus on the Internet-of-Things, Cloud Computing, Network security and Network protocols for wireless, wired, and hybrid networks integrating wireless and wired technologies and protocols.