Data Mining Application
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A Data Mining Application is a specific computing application that is critically dependent on the successful solution of a data mining task.
- AKA: Data Analytics Application, Data Mining Project.
- Context:
- It can be a Predictive Data Mining Application.
- It can be analyzed by a Data Mining Case Study.
- …
- Example(s):
- a Recommendation Application (an instance of a Recommendation Task), such as: Netflix's Movie Recommendation System.
- a Credit Scoring Application (an instance of a Credit Scoring Task/Credit Risk Analysis).
- a Fraud Detection Application ((an instance of a Fraud Detection Task).
- an Intruder Detection Application (an instance of an Intruder Detection Task).
- a Customer Profiling Application (an instance of a Customer Profiling Task).
- a Spam Detection Application (an instance of a Spam Detection Task).
- a Personalization Application.
- …
- Counter-Example(s):
- See: Data Mining Discipline.
References
2009
- http://www.kdnuggets.com/solutions/index.html
- SaaS Analytics, analytics on-demand, analytics in the cloud.
- BI (Business Intelligence), Database and OLAP software
- Bioinformatics and Pharmaceutical solutions
- CRM (Customer Relationship Management)
- Data Providers and Data Cleaning Tools
- eCommerce solutions
- Email analysis, response, and marketing
- Fraud Detection solutions
- Health Care and Human Resources solutions
- Knowledge Management and News
- Microarray data analysis, gene expression analysis
- Personalization solutions
- Privacy software and solutions
- Real-Time Analytics and Decisioning solutions
- Retail solutions
- Risk Analysis and Credit Scoring
- Sports and Entertainment
- Stock and Investment Analysis and Prediction
- Survey creation and analysis
- Telecom
- Travel sites and solutions
- Twitter Analytics sites and solutions.
- Web Advertising
- Web Mining, Web Content Mining
2001
- (Hand et al., 2001) ⇒ David J. Hand, Heikki Mannila, and Padhraic Smyth. (2001). “Principles of Data Mining." MIT Press. ISBN:026208290X
- Rather than discuss specific data mining applications at length (such as, say, collaborative filtering, credit scoring, and fraud detection), we have instead focused on the underlying theory and algorithms that provide the “glue” for such applications. This is not to say that we do not pay attention to the applications.
- (Smyth, 2001) ⇒ Padhraic Smyth. (2001). “Data Mining at the Interface of Computer Science and Statistics.” In: (Grossman et al., 2001)
- The primary conclusion is that closer integration of computational methods with statistical thinking is likely to become increasingly important in data mining applications. … For example, decision trees are perhaps the single most widely-used modeling technique in commercial predictive data mining applications [Joh99, Koh00].