TY  - JOUR
T1  - Frequency-Based Fast Algorithm for Anomaly Detection in Big Data
AU - Hashmi, Adeel S. AU - Ahmad, Tanvir 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 23
SP  - 7389
EP  - 7392
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.7389.7392
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.7389.7392
KW  - Data mining
KW  -distributed computing
KW  -parallel processing
KW  -predictive models
KW  -machine learning
KW  -tools
AB  - Anomaly/outlier detection is an important area of machine learning which finds its application in
intrusion-detection, fraud-detection, etc. In recent times, the focus of data analytics has shifted to big data
analytics, i.e., analytics on large-scale data and fast-moving data streams. The traditional data processing tools
and algorithms are not able to handle big data, so, there is a need of algorithms to be implemented in a parallel
model like MapReduce to solve this problem. In this study, the researchers implement frequency-based
algorithm on Spark MapReduce as a scalable and accurate solution for anomaly detection on large-scale as well
as streaming datasets.
ER  -