A Survey on Prediction of Missing Sensor Data Using Association Rule

Authors

  • Hitarth Chauhan  Department of Information Technology, L. D. Engineering College, Ahmedabad, Gujarat, India
  • Bakul Panchal  Department of Information Technology, L. D. Engineering College, Ahmedabad, Gujarat, India

Keywords:

Window Association Rule Mining, K-nearest Neighbour Estimation, WSN, Data Reduction Mechanism, Data Mining, Sensor Data

Abstract

Missing values is major problem in sensor network. Currently we have many existing approach to predict missing values in stream of data. But for pre fetched existing data we can’t use such techniques. So while querying in such data will lead to wrong results. So in this paper we will try to predict such missing data in existing sensor data using association rule mining techniques.

References

  1. Sneha Arjun Dhargalkar, A.D. Bapat “Determining Missing Values in Dimension Incomplete Databases using Spatial-Temporal Correlation Techniques”, In 2014, IEEE
  2. Mihail Halatchev Le Gruenwald, “Estimating Missing Values in Related Sensor Data Streams” ADVANCES IN DATA MANAGEMENT 2005
  3. Le Gruenwald, Hamed Chok, Mazen Aboukhamis, “Using Data Mining to Estimate Missing Sensor Data” In 2007 IEEE
  4. Anjan Das, “An Enhanced Data Reduction Mechanism to Gather Data for Mining Sensor Association Rules” In 2011 IEEE

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2015-12-25

Issue

Volume 1, Issue 6, November-December-2015

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Hitarth Chauhan, Bakul Panchal, " A Survey on Prediction of Missing Sensor Data Using Association Rule, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 6, pp.182-184, November-December-2015.