Data Mining
Bibliographic Data
| ID | 23739619 |
|---|---|
| Authors | Gregg R Murray (0000-0002-3976-9365, Texas Tech University), Anthony Scime (SUNY Brockport) |
| Year | 2015 |
| Pages | 1-15 |
| Publication date | 2015-05-15 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | OTHER |
| Venue | Emerging Trends in the Social and Behavioral Sciences (JOURNAL) |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/9781118900772.etrds0071 |
| OpenAlex | W4230725485 |
| ISBN | 9781118900772 |
| Language | EN |
| References cited | 25 |
This essay introduces data mining as an analytical technique for novice to professional social and behavioral scientists. It presents data mining, which is also known as, among other things, data analytics and predictive analytics , as an effective tool for researchers who are interested in the analysis of “big data” as well as small, unique data sets. It addresses foundational elements of data mining such as how to avoid “data dredging” and the importance of theory as embodied in researcher domain expertise. It also briefly defines and describes classification analysis, association rules, and clustering, which are the major methodologies among a large number of methodologies that constitute data mining. This essay identifies analytical problems and data for which the techniques are best suited. It goes on to highlight a number of cutting‐edge studies that relied on data mining techniques in disciplines such as criminal justice, education, health sciences, linguistics, political science, and sociology. This essay concludes with a review of key considerations for future research to include discussions of the burgeoning of new analytical techniques and new data sets and sources, the importance and protection of data‐source privacy, and the ethical obligation researchers have to exploit to their fullest extent the costly data on social and behavioral issues collected by scientists and society.
Analytics · Big data · Data Analysis · Data mining · Data science · Domain (mathematical analysis) · Exploit · Computer Science · Crime Patterns and Interventions · Cybercrime and Law Enforcement Studies · Qualitative Comparative Analysis Research
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| Citation velocity | historical |
|---|---|
| Highly cited | No |