Name
Abstract | ||
|---|---|---|
Homicide is one of the most serious kinds of offenses. Research on causes of homicide has never reached a definite conclusion. The purpose of this article is to put homicide in its broad range of social context to seek correlation between this offense and other macroscopic socioeconomic factors. This international-level comparative study used a dataset covering 181 countries and 69 attributes. The data were processed by the Self-Organizing Map SOM assisted by other clustering methods, including ScatterCounter for attribute selection, and several statistical methods for obtaining comparable results. The SOM is found to be a useful tool for mapping criminal phenomena through processing of multivariate data, and correlation can be identified between homicide and socioeconomic factors. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1080/08839514.2015.1016774 | Applied Artificial Intelligence |
Field | DocType | Volume |
Data science,Social environment,Feature selection,Computer security,Computer science,Artificial intelligence,Cluster analysis,Multivariate statistics,Homicide,Self,Business intelligence,Machine learning,Socioeconomic status | Journal | 29 |
Issue | ISSN | Citations |
4 | 0883-9514 | 2 |
PageRank | References | Authors |
0.40 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingan Li | 1 | 13 | 4.37 |
| Henry Joutsijoki | 2 | 46 | 8.41 |
| Jorma Laurikkala | 3 | 345 | 24.82 |
| Markku Siermala | 4 | 55 | 6.34 |
| Martti Juhola | 5 | 456 | 63.94 |