FP-outlier: Frequent pattern based outlier detection

An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from the data set. The outliers are defined as the data transactions that contain less frequent patterns in their itemsets. We define a measure called FPOF (Frequent Pattern Outlier Factor) to detect the outlier transactions and propose the FindFPOF algorithm to discover outliers. The experimental results have shown that our approach outperformed the existing methods on identifying interesting outliers.

• Publication year

  2005

• Venue

  Computer Science and Information Systems

• Publication date

  Unknown publication date

• Fields of study

  Computer Science

• Identifiers
  DOI 10.2298/CSIS0501103H
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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