CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Adult Daily Life Analysis by Detecting Outliers and Top-K Queries

by K. Ashesh, G. Appa Rao
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 9
Year of Publication: 2015
Authors: K. Ashesh, G. Appa Rao
10.5120/ijca2015906460

K. Ashesh, G. Appa Rao . Adult Daily Life Analysis by Detecting Outliers and Top-K Queries. International Journal of Computer Applications. 127, 9 ( October 2015), 11-15. DOI=10.5120/ijca2015906460

@article{ 10.5120/ijca2015906460,
author = { K. Ashesh, G. Appa Rao },
title = { Adult Daily Life Analysis by Detecting Outliers and Top-K Queries },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 9 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 11-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume127/number9/22756-2015906460/ },
doi = { 10.5120/ijca2015906460 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:19:26.254396+05:30
%A K. Ashesh
%A G. Appa Rao
%T Adult Daily Life Analysis by Detecting Outliers and Top-K Queries
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 9
%P 11-15
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Detecting outliers has attracted the attention of researchers ever since it was found useful to uncover latent, unexpected, interested and previously unknown knowledge. There are many real world utilities of outlier detection such as change monitoring, rare event discovery, fraud detection, and event change detection, alarm systems, revealing trend occurrences and finding strange patterns. Time series analysis can help detect outliers and discover knowhow for efficient decision making. Outlier detection mechanisms that came into existence dealt with plethora of problems. In this paper, our focus is on analyzing strange behaviours in activities of daily living. We proposed two approaches towards adult daily life analysis. Both are data mining approaches that extract knowledge from underlying dataset. Top-K probabilistic analysis and outlier detection are the two mechanisms used to analyze adult life. The top-k analysis brings about the prevalent behavioural dynamics in adult life while the outlier detection throws light into observations that are peculiar and do not match with other observations. We used a time series dataset named Activities of Daily Living (ADLs) collected from UCI machine learning repository. We built a prototype to demonstrate the proof of concept. The empirical results are encouraging.

References
  1. R.Parimaladev. (2012). PROJECTIVE CLUSTERING FOR OUTLIER DETECTION IN HIGH DIMENSIONAL DATASET. ISSN. 1 (9), p.23-30.
  2. WADA, Kazumi. (2012). Parallel computation of modified Stahel–Donoho estimators for multivariate outlier detection. detection.p.44-55.
  3. Mark j.p. (2010). Distribution based outlier detection in univariate.IJSIM.p.56-63.
  4. Valentin Todorov. (2004). Detection of Multivariate Outliers in Business Survey Data with Incomplete Information. Nations Industrial Development Organization.p.66-77.
  5. Tan, Steinbach, Kumar. (2008). Data Mining Anomaly Detection. Tan, Steinbach, Kumar.p.88-99.
  6. Sanjay Chawla and Pei Sun. (2006). Outlier Detection: Principles, Techniques and Applications. pakkd.p.56-70.
  7. L Y Ji, Y J Yang. (1996). Robust Time Series Processing for Heart Rate Variability Analysis in Daily Life. ISSN. p.77-88.
  8. Samson Sifael Kiware. (2009). Detection of Outliers in TimeSeriesData. detection.p.66-78.
  9. Manish Gupta. (2014). Outlier Detection for Temporal Data: A Survey.IEEE. 25 (4), p.77-88.
  10. Victoria J. Hodge. (2004). Survey of Outlier Detection Methodologies.victort. p.23-30.
  11. Hans-Peter Krieg. (1949). Outlier Detection Techniques. IJSIM. p.56-70.
  12. Meike Klettke. (2013). Schema Extraction and Structural Outlier Detection for JSON-based NoSQL Data Stores. detection.p.77-88.
  13. Spiros Papadimitriou. (2003). LOCI: Fast Outlier Detection Using the Local Correlation Integral. IEEE. 5 (8), p.23-30.
  14. Karanjit Singh. (2012). Outlier Detection: Applications And Techniques.IJSIM. 9 (3), p.44-55.
  15. Irad Ben-Gal. (2004). OUTLIER DETECTION. detection. p.77-88.
  16. Varun Chandola. (2006). Outlier Detection : A Survey. detection. p.66-78.
  17. Xin He,. (2007). An Outlier Detection Based Approach for PCB Testing.colostae. p.88-99.
  18. CHARU C. AGGARWAL. (2006). OUTLIER ANALYSIS. IBMT. p.54-66.
  19. Thomas Seidl. (2011). Statistical selection of relevant subspace projections for outlier ranking. IJSIM. p.78-99.
  20. Jun-ichi Takeuchi. (2006). A Unifying Framework for Detecting Outliers and Change Points from Time Series. IEEE. 18 (4), p.44-55.
  21. Kaouther Nouira. (2006). Time Series Analysis and Outlier Detection in Intensive Care Data. IEEE.p.34-45.
  22. Liu Fang. (2011). An online outlier detection method for process control time series. IEEE. p.45-55.
  23. Zhi Li. (2006). A Unifying Method for Outlier and Change Detection from Data Streams. IEEE.p.66-75.
  24. Guoming Tang. (2014). From Landscape to Portrait: A New Approach for Outlier Detection in Load Curve Data. IEEE. 5 (4), p.45-60.
  25. Faraz Rasheed and Reda Alhajj. (2014). A Framework for Periodic Outlier Pattern Detection in Time-Series Sequences. IEEE. 44 (5), p.44-55.
  26. Hancong Liu. (2004). On-line outlier detection and data cleaning.elsver. 25 (1), p.1635–1647.
  27. Baidya Nath Saha,. (2009). Snake Validation: A PCA-Based Outlier Detection Method. IEEE. 16 (6), p.77-88.
  28. Joanne B.; Ciulla. (2013). Introduction to Outlier Detection. (www.biomedware.com).p.44-55.
  29. Mary Kathryn Thompson. (2013). Statistical Outlier Detection for Jury Based Grading Systems. Atalnta. n.d (n.d), p.55-66.
  30. Hans-Peter Kriegel. (2010). Outlier Detection Techniques.http://www.dbs.ifi.lmu.de. p.77-88.
  31. Fei Wang. (2013). Latent Outlier Detection and the Low Precision Problem. IJSIM. p.34-45.
  32. Kevin Bache. (2007). Machine Learning Repository. UCI. p.12-19. Retrieved from https://archive.ics.uci.edu/ml/datasets.html.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining outlier detection top-k queries adult life analysis

Powered by PhDFocusTM