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Discussion on: Identify and describe some approaches to Data Mining and Analytics

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ujjwal_poudel

O’Brien & Marakas (2006), in their book Introduction to information systems, data mining approaches and statistical techniques used to predict future behaviour, especially to unlock the value of business intelligence for strategy. Data in data warehouses are analyzed to reveal hidden patterns and trends likes’ market-basket analysis to identify new product bundles, find root cause of qualify or manufacturing problems, prevent customer attrition, acquire new customers, cross-sell to existing customers, and profile customers with more accuracy.

According to Kantardzic (2011), the goals of prediction and description are achieved by using data mining techniques for the following primary data - mining tasks:

  1. Classification: Discovery of a predictive learning function that classifies a data item into one of several predefined classes. Here, it assigns items in a collection to target categories. For instance, the customer of bank asking for loan should be analyzing their profile first. The manager identifies loan applicants as low, medium, or high credit risks.

  2. Regression: Discovery of a predictive learning function that maps a data item to a real value prediction variable. Profit, sales, mortgage rates, house values, square footage, temperature, or distance could all be predicted using regression techniques.

  3. Clustering: A common descriptive task in which one seeks to identify a finite set of categories or clusters to describe the data. This analysis is broadly used in many applications such as market research, pattern recognition, data analysis, and image processing.

  4. Summarization: An additional descriptive task that involves methods for finding a compact description for a set (or subset) of data. Excel is a best tool to summarize the data and the use of formula can interpret the relation. This is applied for data analysis, data visualization and automated report generation.

  5. Dependency Modeling: Finding a local model that describes significant dependencies between variables or between the values of a feature in a data set or in a part of a data set. Retailers use dependency modeling to analyze consumer behaviour such as purchasing habits.

  6. Change and Deviation Detection: Discovering the most significant changes in the data set. It focuses on discovering the most significant changes in the data from previously measured or normative values.

References

Kantardzic, M. (2011). Data mining: concepts, models, methods, and algorithms . John Wiley & Sons.

O’Brien, J. A., & Marakas, G. M. (2006). Management information systems (Vol. 6). McGraw-Hill Irwin.