Outlier Analysis Using Frequent Pattern Mining (LOF Algorithm)
Ankur N. Yagnik,  Maulik V Dhamecha
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 item sets) from the data set. The outliers are defined as the data transactions that contain less frequent patterns in their item sets. We define a measure called FPOF (Frequent Pattern Outlier Factor) to detect the outlier transactions and propose the Find FPOF algorithm to discover outliers. The experimental results have shown that our approach outperformed the existing methods on identifying interesting outliers.
Keywords- Frequent pattern mining ,Association rules ,Data mining research, Applications, FPOF, abnormalities, discordant, deviants, anomalies
Cite this Article
Ankur N. Yagnik,  Maulik V Dhamecha,   "Outlier Analysis Using Frequent Pattern Mining (LOF Algorithm)"
, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.2, Issue 2, pp.2083-2089, June 2014, Available at :http://www.ijedr.org/papers/IJEDR1402127.pdf