An Efficient Strategy to Detect Outlier Transactions
Anjali Barmade1, Madhu M.Nashipudinath2
1Anjali Barmade, Department of Computer Department, PIIT, Mumbai, India
2Madhu M. Nashipudinath, Department of Computer Department, PIIT, Mumbai, India.
Manuscript received on December 08, 2014. | Revised Manuscript received on December 15, 2014. | Manuscript published on January 05, 2014. | PP: 174-178 | Volume-3 Issue-6, January 2014. | Retrieval Number: F2037013614 /2014©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Instant identification of outlier patterns is very important in modern-day engineering problems such as credit card fraud detection and network intrusion detection. Most previous studies focused on finding outliers that are hidden in numerical datasets. Unfortunately, those outlier detection methods were not directly applicable to real life transaction databases. .Outlier detection methods are divided into transaction specific and non transaction specific outlier detection methods, In these paper we are going to focus mainly on transaction specific methods and detect outlier transactions from transactional databases e.g. purchase of the data at the store, customer dataset at a company. Here we are going to compare two transaction specific methods and find efficient method from them
Keywords: outlierdetection, transactional databases, association rule, frequent pattern