June 2011
Beginner to intermediate
744 pages
25h 11m
English
Content preview from Data Mining: Concepts and Techniques, 3rd EditionBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Publisher Summary
This chapter aims to study outlier detection techniques. The different types of outliers are defined. An overview of outlier detection methods is also presented. Assume that a given statistical process is used to generate a set of data objects. An outlier is a data object that deviates significantly from the rest of the objects, as if it were generated by a different mechanism. Types of outliers include global outliers, contextual outliers, and collective outliers. An object may be more than one type of outlier. Outlier detection (also known as anomaly detection) is the process of finding data objects with behaviors that are very different from expectation. Outlier detection is important in many applications in addition to fraud ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.