Fuzzy c-means clustering based outlier detection for SAW electronic nose

The paper presents a novel way of using fuzzy c means clustering in supervised manner for outlier detection in surface acoustic wave sensor array based electronic nose. The method is trained through the set of known class labels samples taken as train set combined with the test set samples, which may contain outlier class and/or true data samples. Fuzzy c means clustering is implemented with equating cluster number equal to one class greater than the true data classes actually present in the train data set. A criteria of outlier detection based on the sum of membership grades of the train data set samples has been developed. The obtained results show that the proposed fuzzy c means clustering based outlier detection method detects the true data samples and outliers quite efficiently.

[1]  Mamta Khaneja,et al.  Multifrequency characterization of viscoelastic polymers and vapor sensing based on SAW oscillators. , 2009, Ultrasonics.

[2]  J. Micklefield,et al.  The optimisation of sorption sensor arrays for use in ambient conditions , 1998 .

[3]  Christian Borgelt,et al.  Effects of Irrelevant Attributes in Fuzzy Clustering , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[4]  G. Raju,et al.  A Novel Fuzzy Clustering Method for Outlier Detection in Data Mining , 2009 .

[5]  J. Cooper,et al.  All-Digital Interface ASIC for a QCM-Based Electronic Nose , 2004 .

[6]  R. Chaudhary,et al.  Solvation, transduction and independent component analysis for pattern recognition in SAW electronic nose , 2006 .

[7]  Michael H. Abraham,et al.  Scales of solute hydrogen-bonding: their construction and application to physicochemical and biochemical processes , 2010 .

[8]  Jay W. Grate,et al.  Acoustic Wave Microsensor Arrays for Vapor Sensing , 2000 .

[9]  Stephanie Thalberg,et al.  Acoustic Wave Sensors Theory Design And Physico Chemical Applications , 2016 .

[10]  Charu C. Aggarwal,et al.  Outlier Detection for Temporal Data: A Survey , 2014, IEEE Transactions on Knowledge and Data Engineering.

[11]  J. Ferry.  Viscoelastic properties of polymers , 1961 .

[12]  Stephen J. Martin,et al.  Dynamics and Response of Polymer-Coated Surface Acoustic Wave Devices: Effect of Viscoelastic Properties and Film Resonance , 1994 .

[13]  Douglas M. Hawkins.  Identification of Outliers , 1980, Monographs on Applied Probability and Statistics.

[14]  J. Grate,et al.  Design and Information Content of Arrays of Sorption-Based Vapor Sensors Using Solubility Interactions and Linear Solvation Energy Relationships , 2009 .

[15]  Kuo-Lung Wu,et al.  Analysis of parameter selections for fuzzy c-means , 2012, Pattern Recognit..

[16]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[17]  E. Zellers,et al.  Vapor recognition with small arrays of polymer-coated microsensors. A comprehensive analysis. , 1999, Analytical chemistry.

[18]  R. D. S. Yadava,et al.  Detection of outliers in surface acoustic wave (SAW) chemical sensor array responses by one-class support vector machine , 2011, 2011 IEEE Recent Advances in Intelligent Computational Systems.

[19]  K. Yamanaka,et al.  Fabrication of Thin Sensitive Film of Ball Surface Acoustic Wave Sensor by Off-Axis Spin-Coating Method , 2009 .

[20]  Nirvana Meratnia,et al.  Outlier Detection Techniques for Wireless Sensor Networks: A Survey , 2008, IEEE Communications Surveys & Tutorials.

[21]  S. Rose-Pehrsson,et al.  Improved probabilistic neural network algorithm for chemical sensor array pattern recognition. , 1999, Analytical chemistry.

[22]  S.Joe Qin,et al.  Neural Networks for Intelligent Sensors and Control — Practical Issues and Some Solutions , 1997 .

[23]  J. Grate,et al.  Hydrogen bond acidic polymers for surface acoustic wave vapor sensors and arrays. , 1999, Analytical chemistry.

[24]  Irad Ben-Gal.  Outlier Detection , 2005, The Data Mining and Knowledge Discovery Handbook.

[25]  R.D.S. Yadava,et al.  Preprocessing of SAW Sensor Array Data and Pattern Recognition , 2009, IEEE Sensors Journal.

[26]  A. Sorsa,et al.  Sensor Validation And Outlier Detection Using Fuzzy Limits , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[27]  K. S. Rawlinson,et al.  Development of a Surface Acoustic Wave Sensor for In-Situ Monitoring of Volatile Organic Compounds , 2003 .

[28]  Osman Hegazy,et al.  Outliers detection and classification in wireless sensor networks , 2013 .

[29]  M. Alamă,et al.  Characterization of the ability of polymeric chemiresistor arrays to quantitate trichloroethylene using partial least squares ( PLS ) : effects of experimental design , humidity , and temperature , 2002 .

[30]  Mia Hubert,et al.  Robustness and Outlier Detection in Chemometrics , 2006 .

[31]  Jiri Janata,et al.  Principles of Chemical Sensors , 1989 .

[32]  Vic Barnett,et al.  Outliers in Statistical Data , 1980 .

[33]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[34]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[35]  W. Chimphlee,et al.  Implementation of Fuzzy c-Means and Outlier Detection for Intrusion Detection with KDD Cup 1999 Data Set , 2012 .