Data Mining - Study24x7
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Data Mining
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Four stages of data mining :
Data Sources
Data Exploration
Modeling
Deploying Models

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How data mining is useful for Analytics Websites ? There are different analytics websites which will compare the prices of different things from other website. The Analytics and data mining plays big role in that websites. If you check the website named Trivago which will gives the information of di...

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How data mining is useful for mobile service providers ? The Mobile service providers uses huge data mining to collect customer data.Mobile phone and utilities companies use Data Mining and Business Intelligence to predict ‘churn’, the terms they use for when a customer leaves their company to g...

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What is difference between data warehouse and data mining? Data Warehousing: It is a process where the data is extracted from various sources. Further, the data is cleansed and stored. Data Mining: It is a process where it explores the data using the queries.Basically, the queries are used to explor...

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What Are Cubes? A data cube stores data in a summarized version which helps in a faster analysis of data. The data is stored in such a way that it allows reporting easily.

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What do you mean by Cluster Analysis? Cluster analysis is an important human activity . Cluster analysis has been widely used in numerous application , including market research, pattern recognition, data analysis and image processing.

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What do you mean by Analytical characterization? Data mining has proved to be an effective means of integrating the analyzing the diverse collection of variables. If the mined concept descriptions involve many attribute, an analytical characterization should be performed.

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What is Data Aggregation and Generalization? Aggregation is the technique wherein, as the name suggest, summary or aggregation operation are applied to the data for example, we can construct a data cube for analysis of the data at multiple granularity. Generalizing the data basically deals with the ...

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What is Data Transformation ? It consist of following tasks : Generalization - concept hierarchy climbing, Attribute/feature construction - New attributes are constructed and added to the tuple, Normalization - scaled to fall within a small, specified range

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