Divisions and Categories of Business Intelligence Technologies - Assignment Example

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An author of this assignment "Divisions and Categories of Business Intelligence Technologies" would shed light on the concept of business intelligence. Furthermore, the assignment provides a description of the most widely used techniques in such a field…
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Download file to see previous pages Knowledge discovery in databases (KDD) process is generally defined using the following stages: selection, preprocessing, transformation, data mining, and interpretation. However, it exists in many variations of this theme such as the Cross-Industry Standard Process for Data Mining (CRISP-DM). This theme defines knowledge discovery in six phases; understanding of business, understanding of data, preparation of data, modeling, evaluation, and deployment of the results (Ling Liu & Tamer 2009). Another example of theme follows a simplified process such as pre-processing of data, mining of data and validating the results obtained.
Pre-processing involves assembling a target data- since data mining only covers the patterns that are essentially present in the data, the dataset targeted should be e big enough to hold these patterns while at the same time remaining brief enough to be extracted within an acceptable timeframe. Common data sources are data warehouses and data marts. Pre-processing of data is indispensable in the analysis multivariate datasets before the mining of data. Therefore, the target set is cleaned. The data cleaning involves the removal of all observations containing the missing data as well as noise.
Data mining engage six general groups of tasks. The first task is anomaly detection- this refers to the identification of extraordinary but interesting data records or erroneous data that needs further investigation. Secondly, an association search is the next task. This task involves the relationship existing between variables (Ling Liu & Tamer 2009). ...Download file to see next pagesRead More
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