Title :
Frequent pattern-based outlier detection measurements: A survey
Author :
Said, Aiman Moyaid ; Dominic, Dhanapal Durai ; Samir, Brahim Belhaouari
Author_Institution :
Fac. of Sci. & Inf. Technol., Univ. Teknol. PETRONAS, Tronoh, Malaysia
Abstract :
Outlier detection is one of the main data mining tasks. The outliers in data are more significant and interesting than common ones in a wide variety of application domains. Recently, a new trend for detecting the outlier by discovering frequent patterns (or frequent itemsets) from the data set has been studies. In this paper, we present a summarization study of the available outlier detection measurements which are based on the frequent patterns discovery.
Keywords :
data mining; statistical analysis; data mining; frequent itemset discovery; frequent pattern-based outlier detection; frequent patterns discovery; Accuracy; Data mining; Itemsets; Length measurement; Object recognition; Weight measurement; frequent pattern mining; outlier detection; outlier measurement;
Conference_Titel :
Research and Innovation in Information Systems (ICRIIS), 2011 International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-61284-295-0
DOI :
10.1109/ICRIIS.2011.6125705
