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18 Σεπ 2024 · A Data-Warehouse is a heterogeneous collection of data sources organized under a unified schema. There are 2 approaches for constructing a data warehouse: The top-down approach and the Bottom-up approach are explained below. What is Top-Down Approach?
17 Απρ 2024 · The analysis of outlier data is referred to as outlier analysis or outlier mining. An outlier cannot be termed as a noise or error. Instead, they are suspected of not being generated by the same method as the rest of the data objects. Outliers are of three types, namely –. Global (or Point) Outliers.
Architecture of KDD Data Warehouse: A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process.
12.1 Outliers and Outlier Analysis. Let us first define what outliers are, categorize the different types of outliers, and then discuss the challenges in outlier detection at a general level.
4 Μαρ 2022 · Outlier Detection: Outlier Detection is a natural extension of data mining techniques. As Data Mining is the extraction of general patterns or trends in large datasets, outlier detection is the discovery of data objects that deviate significantly from such general patterns or trends.
Data Warehouse Design Process: A data warehouse can be built using a top-down approach, a bottom-up approach, or a combination of both. The top-down approach starts with the overall design and planning. It is useful in cases where the technology is mature and well known, and where the business problems that must be
12.7 Mining Contextual and Collective Outliers. An object in a given data set is a contextual outlier (or conditional outlier) if it deviates significantly with respect to a specific context of the object (Section 12.1). The context is defined using contextual attributes.
