Question: can you please provide another examples for data reduction, novelty detection, profiling, market basket analysis and sequence analysis Times New... v 11 A A Aa

can you please provide another examples for data can you please provide another examples for data reduction, novelty detection, profiling, market basket analysis and sequence analysis
Times New... v 11 A A Aa Ap s Styles Styles Pane Sensitivity BIU XX AKA 2 Office Update To keep up-to-date with security updates, fixes, and improvements, choose Check for... Check for Updates scurity made Data reduction is the most ubiquitous application, that is, exploiting patterns in data to create a more compact representation of the original. Though vastly broader in scope, data reduction includes analytic ant by methods such as cluster analysis. ne in th luct of re Novelty detection methods seek unique or previously unobserved data patterns. The methods find gments, application in business, science, and engineering. Business applications include fraud detection, p profiles warranty claims analysis, and general business process monitoring. ant by th Profiling is a by-product of reduction methods such as cluster analysis. The idea is to create rules that at than th isolate clusters or segments, often based on demographic or behavioral measurements. A marketing sls, or ass analyst might develop profiles of a customer database to describe the consumers of a company's baskets) cted. A sto products. Market basket analysis, or association rule discovery, is used to analyze streams of transaction data by the Part (for example, market baskets) for combinations of items that occur (or do not occur more or less) the one in commonly than expected. Retailers can use this as a way to identity interesting combinations of an extensic purchases or as predictors of customer segments. transactions Sequence analysis is an extension of market basket analysis to include a time dimension to the only than ex analysis. In this way, transactions data is examined for sequences of items that occur (or do not occur) more (or less) commonly than expected. A webmaster might use sequence analysis to identify patterns se between e different th: or problems of navigation through a website. known as cher lables. It is a da clusters. Uns

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