Autoregressive model-based fuzzy clustering and its application for detecting information redundancy in air pollution monitoring networks

Soft Computing

Volumn 17, Issue 1, 2013, Pages 83-131

  D'Urso, Pierpaolo ^a   Di Lallo, Dario ^a   Maharaj, Elizabeth Ann ^b  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

^b MONASH UNIVERSITY   (Australia)

Author keywords

Air pollution monitoring network; Autoregressive process; Fuzzy C Medoids clustering; Monitoring efficiency; Redundancy analysis of environmental monitoring networks; Time series clustering

Indexed keywords

AIR POLLUTION MONITORING; AUTO REGRESSIVE PROCESS; ENVIRONMENTAL MONITORING; FUZZY C-MEDOIDS CLUSTERING; TIME SERIES CLUSTERING;

AIR POLLUTION; C (PROGRAMMING LANGUAGE); CARBON DIOXIDE; CLUSTERING ALGORITHMS; FUZZY CLUSTERING; REDUNDANCY; TIME SERIES;

MONITORING;

EID: 84871883497     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-012-0905-6     Document Type: Article

References (101)

1. Alaeddini A, Ghazanfari M, Nayeri MA (2009) A hybrid fuzzy-statistical clustering approach for estimating the time of changes in fixed and variable sampling control charts. Inf Sci 179: 1769-1784.
2. Alonso AM, Berrendero JR, Hernández A, Justel A (2006) Time series clustering based on forecast density. Comput Stat Data Anal 51(2): 762-776.
3. Anttila P, Tuovinen J-P (2010) Trends of primary and secondary pollutant concentrations in Finland in 1994-2007. Atmos Environ 44: 30-41.
4. Ausloos MA, Gligor M (2008) Cluster expansion method for evolving weighted networks having vector-like nodes. Acta Phys Polon A 114: 491-499.
5. Ausloos M, Lambiotte R (2007) Clusters or networks of economies? A macroeconomy study through gross domestic product. Physica A 382: 16-21.
6. Aznarte JLM, Benítez Sánchez JM, Lugilde DN, de Linares Fernández C, Díaz de la Guardia C, Sánchez FA (2007) Forecasting airborne pollen concentration time series with neural and nuro-fuzzy models. Exp Syst Appl 32: 1218-1225.
7. Barioni MCN, Razente HL, Traina AJM, Traina C (2008) Accelerating k-medoid-based algorithms through metric access methods. J Syst Softw 8: 343-355.
8. Basalto N, Bellotti R, De Carlo F, Facchi P, Pantaleo E, Pascazio S (2007) Hausdorff clustering of financial time series. Physica A 379: 635-644.
9. Bengtsson T, Cavanaugh JE (2008) State-space discrimination and clustering of atmospheric time series data based on Kullback information measures. Environmetrics 19: 103-121.
10. Bohm M, McCune B, Vandetta T (1991) Diurnal curves of tropospheric ozone in the western United States. Atmos Environ 25: 1570-1590.
11. Caiado J, Crato N, Peña D (2006) A periodogram-based metric for time series classification. Comput Stat Data Anal 50: 2668-2684.
12. Caiado J, Crato N, Peña D (2009) Comparison of times series with unequal length in the frequency domain. Commun Stat Simul Comput 38(3): 527-540.
13. Campello RJGB, Hruschka ER (2006) A fuzzy extension of the silhouette width criterion for cluster analysis. Fuzzy Sets Syst 157: 2858-2875.
14. Coppi R, D'Urso P (2003) Three-way fuzzy clustering models for LR fuzzy time trajectories. Comput Stat Data Anal 43: 149-177.
15. Coppi R, D'Urso P (2006) Fuzzy unsupervised classification of multivariate time trajectories with the Shannon entropy regularization. Computational Statistics & Data Analysis 50(6): 1452-1477.
16. Coppi R, D'Urso P, Giordani P (2004) Informational paradigm and entropy-based dynamic clustering in a complete fuzzy framework. In: Angeles Gil M, Lopez-Diaz MC, Grzegorzewski P (eds) Soft methodology in random information systems. Springer, Heidelberg, pp 463-470.
17. Coppi R, D'Urso P, Giordani P (2006a) Fuzzy C-Medoids Clustering Models for time-varying data. In: Bouchon-Meunier B, Coletti G, Yager RR (eds) Modern information processing: from theory to applications. Elsevier, Amsterdam, pp 195-206.
18. Coppi R, D'Urso P, Giordani P (2006b) Fuzzy K-Medoids Clustering Models for fuzzy multivariate time trajectories. In: Rizzi A, Vichi M (eds) COMPSTAT 2006, Rome, 28 August-1 September 2006. Proceeding in computational statistics. Physica-Verlag, pp 17-29.
19. Coppi R, D'Urso P, Giordani P (2010) A fuzzy clustering model for multivariate spatial time series. J Classif 27: 54-88.
20. Corduas M, Piccolo D (2008) Time series clustering and classification by the autoregressive metric. Comput Stat Data Anal 52(4): 1860-1872.
21. Costanzo GD (2001) A Constrained k-means clustering algorithm for classifying spatial units. Stat Methods Appl 10: 237-256.
22. D'Urso P (2004) Fuzzy C-means clustering models for multivariate time-varying data: different approaches. Int J Uncertain Fuzziness Knowledge-Based Syst 12(3): 287-326.
23. D'Urso P (2005) Fuzzy clustering for data time arrays with inlier and outlier time trajectories. IEEE Trans Fuzzy Syst 13(5): 583-604.
24. D'Urso P, Giordani P (2006) A weighted fuzzy c-means clustering model for fuzzy data. Comput Stat Data Anal 50(6): 1496-1523.
25. D'Urso P, Maharaj EA (2009) Autocorrelation-based fuzzy clustering of time series. Fuzzy Sets Syst 160: 3565-3589.
26. Dembélé K (2003) Fuzzy C-means method for clustering microarray data. Bioinformatics 19: 973-980.
27. Dorling SR, Davies TD, Pierce CE (1992) Cluster analysis: a technique for estimating the synoptic meteorological controls on air and precipitation chemistry-method and applications. Atmos Environ 26: 2575-2581.
28. Dose C, Cincotti S (2005) Clustering of financial time series with application to index and enhanced-index tracking portfolio. Physica A 355: 145-151.
29. Everitt BS, Landau S, Leese M (2001) Cluster analysis, 4th edn. Arnold Press, London.
30. Fadili MJ, Ruan S, Bloyet D, Mazoyer B (2000) A multistep unsupervised fuzzy clustering analysis of fMRI time series. Hum Brain Mapping 10(4): 160-178.
31. Fu KS (1982) Syntactic pattern recognition and applications. Academic Press, San Diego.
32. Gabusi V, Volta M (2005) A methodology for seasonal photochemical model simulation assessment. J Environ Pollut 24: 11-21.
33. Garcia-Escudero LA, Gordaliza A (2005) A proposal for robust curve clustering. J Classif 22: 185-201.
34. Gligor M, Ausloos MA (2008) Convergence and cluster structures in EU area according to fluctuations in macroeconomic indices. J Econ Integration 23: 297-330.
35. 10 and Ozone data. Atmos Environ 40: 5464-5475.
36. Hassanzadeh S, Hosseinibalam F, Alizadeh R (2009) Statistical models and time series forecasting of sulfur dioxide: a case study Tehran. Environ Monit Assess 155: 149-155.
37. Heiser WJ, Groenen PJF (1997) Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima. Psychometrika 62: 63-83.
38. Hwang H, De Sarbo WS, Takane Y (2007) Fuzzy clusterwise generalized structured component analysis. Psychometrika 72: 181-198.
39. Ibarra-Berastegi G, Sáenz J, Ezcurra A, Ganzedo U, de Argandoña JD, Errasti I, Fernandez-Ferrero A, Polanco-Martínez J (2009) Assessing spatial variability of SO2 field as detected by an air quality network using self-organizing maps, cluster, and principal component analysis. Atmos Environ 43: 3829-3836.
40. Ignaccolo R, Ghigo S, Giovenali E (2008) Analysis of monitoring networks by functional clustering. Environmetrics 62: 672-686.
41. Ionescu A, Candau Y, Mayer E, Colda I (2000) Analytical determination and classification of pollutant concentration fields using air pollution monitoring network data-methodology and application in the Paris area, during episodes with peak nitrogen dioxide levels. Environ Model Softw 15: 565-573.
42. 2. http://files. abstractsonline. com/CTRL/51/8/223/401/82C/47F/E9B/3CA/C0C/4C9/F43/1A/a1172_1. doc.
43. James G, Sugar C (2003) Clustering for sparsely sampled functional data. J Am Stat Assoc 98(462): 397-408.
44. Kakizawa Y, Shumway H, Taniguchi M (1998) Discriminant and clustering for multivariate time series. J Am Stat Assoc 93: 328-340.
45. Kamdar T, Joshi A (2000) On creating adaptive Web servers using Weblog Mining. Technical report TR-CS-00-05, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County.
46. Karaca F, Camci F (2010) Distant source contributions to PM10 profile evaluated by SOM based cluster analysis of air mass trajectory sets. Atmos Environ 44: 892-899.
47. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York.
48. Krishnapuram R, Joshi A, Nasraoui O, Yi L (2001) Low-complexity fuzzy relational clustering algorithms for web mining. IEEE IEEE Trans Fuzzy Syst 9(4): 595-607.
49. Krishnapuram R, Joshi A, Yi L (1999) A fuzzy relative of the k-Medoids algorithm with application to Web document and Snippet clustering. IEEE international fuzzy systems conference (FUZZIEEE99), Seoul, pp 1281-1286.
50. Kumar U, Jain VK (2010) ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO). Stoch Environ Res Risk Assess 24: 751-760.
51. Kwon SH (1998) Cluster validity index for fuzzy clustering. Electron Lett 34(22): 2176-2177.
52. Lau J, Hung WT, Cheung CS (2009) Interpretation of air quality in relation to monitoring station's surroundings. Atmos Environ 43: 769-777.
53. Lavecchia C, Angelino E, Bedogni M, Brevetti E, Gualdi R, Lanzani G, Musitelli A, Valentini M (1996) The ozone patterns in the aerological basin of Milan (Italy). Environ Softw 11: 73-80.
54. Liao TW (2005) Clustering of time series data-a survey. Pattern Recognit 38: 1857-1874.
55. Liao TW (2007) A clustering procedure for exploratory mining of vector time series. Pattern Recognit 40: 2550-2562.
56. Lu W-Z, He H-D, Dong L-Y (2011) Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis. Build Environ 46: 577-583.
57. Ludwig FL, Jiang J, Chen J (1995) Classification of ozone and heather patterns associated with high ozone concentrations in the San Francisco and Monterey Bay areas. Atmos Environ 29: 2915-2928.
58. Maharaj EA (1996) A significance test for classifying ARMA models. J Stat Comput Simul 54: 305-331.
59. Maharaj EA (1999) Comparison and classification of stationary multivariate time series. Pattern Recognit 32: 1129-1138.
60. Maharaj EA (2000) Clusters of time series. J Classif 17: 297-314.
61. Maharaj EA (2002) Comparison of non-stationary time series in the frequency domain. Comput Stat Data Anal 40: 131-141.
62. Maharaj EA, Alonso AM (2007) Discrimination of locally stationary time series using wavelets. Comput Stat Data Anal 52(2): 879-895.
63. Maharaj EA, D'Urso P (2010) Coherence-based approach for the pattern recognition of time series. Physica A 389: 3516-3537.
64. Maharaj EA, D'Urso P (2011) Fuzzy clustering of time series in the frequency domain. Inf Sci 181: 1187-1211.
65. Maharaj EA, D'Urso P, Galagedera DUA (2010) Wavelets-based fuzzy clustering of time series. J Classif 27: 231-275.
66. McBratney AB, Moore AW (1985) Application of fuzzy sets to climatic classification. Agric For Meteorol 35: 165-185.
67. Miranda J, Cahill TA, Morales J, Roberto AF, Flores MJ, Diaz RV (1994) Determination of elemental concentrations in atmospheric aerosols in Mexico City using proton induced x-ray emission, proton elastic scattering, and laser absorption. Atmos Environ 28: 2299-2306.
68. Miskiewicz J, Ausloos M (2008) Correlation measure to detect time series distances, whence economy globalization. Physica A 387: 6584-6594.
69. Mitra S (2004) An evolutionary rough partitive clustering. Pattern Recognit Lett 25: 1439-1449.
70. Morlini I (2007) Searching for structure in measurements of air pollutant concentration. Environmetrics 18: 823-840.
71. Ortuño C, Jaimes M, Muñoz R, Ramos R, Paramo VH. Redundancy analysis for the Mexico City air monitoring network: the case of CO. http://files. abstractsonline. com/CTRL/2D/A/06E/7F9/022/434/F8D/F8C/2D3/E4B/F3E/66/a1177_1. doc.
72. Otranto E (2008) Clustering heteroskedastic time series by model-based procedures. Comput Stat Data Anal 52(10): 4685-4698.
73. Pastres R, Pastore A, Tonellato SF (2011) Looking for similar patterns among monitoring stations. Venice Lagoon application. Environmetrics (in press).
74. Pértega Diaz S, Vilar JA (2010) Comparing several parametric and nonparametric approaches to time series clustering: a simulation study. J Classif 27: 333-362.
75. Piccolo D (1990) A distance measure for classifying ARIMA models. J Time Ser Anal 11(2): 153-164.
76. Piccolo D (2007) Statistical issues on the AR metric in time series analysis. In: Proceedings of the Italian Statistical Society, conference on risk and prediction, 6-8 June 2007, pp 221-232.
77. Pires JCM, Sousa SIV, Pereira MC, Alvim-Ferraz MCM, Martins FG (2008a) Management of air quality monitoring using principal component and cluster analysis-Part I: SO2 and PM10. Atmos Environ 42: 1249-1260.
78. Pires JCM, Sousa SIV, Pereira MC, Alvim-Ferraz MCM, Martins FG (2008b) Management of air quality monitoring using principal component and cluster analysis-Part II: CO, NO2 and O3. Atmos Environ 42: 1261-1274.
79. Ramoni M, Sebastiani P, Cohen P (2002) Bayesian clustering by dynamics. Mach Learn 47: 91-121.
80. Romo-Groger CM, Morales JR, Dinator MI, Llona F (1994) Heavy metals in the atmosphere coming from a copper smelter in Chile. Atmos Environ 28: 705-711.
81. Ruijgrok W, Romer FG (1993) Aspects of wet, acidifying deposition in Arnhem: source regions, correlations, and trends. Atmos Environ 27: 637-653.
82. Runkler TA, Bezdek JC (1999) Ace: a tool for clustering and rule extraction. IEEE Trans Fuzzy Syst 5(2): 270-293.
83. Saksena S, Joshi V, Patil RS (2003) Cluster analysis of Delhi's ambient air quality data. J Environ Monit 5: 91-499.
84. Sanchez Gomez ML, Ramos Martin MC (1987) Application of cluster analysis to identify sources of airborne particles. Atmos Environ 21: 1521-1527.
85. Sanchez ML, Pascual D, Ramos C, Perez I (1990) Forecasting particulate pollutant concentrations in a city from meteorological variables and regional weather patterns. Atmos Environ 26: 1509-1519.
86. Savvides A, Promponas VJ, Fokianos K (2008) Clustering of biological time series by cepstral coefficients based distances. Pattern Recognit 41: 2398-2412.
87. Shaw CT, King GP (1992) Using cluster analysis to classify time series. Physica D Non Linear Phenom 58: 288-298.
88. Silva C, Quiroz A (2003) Optimization of the atmospheric pollution monitoring network at Santiago de Chile. Atmos Environ 37: 2337-2345.
89. Tarpey T, Kinateder KKJ (2003) Clustering functional data. J Classif 20: 93-114.
90. Toulemonde G, Guillou A, Naveau P, Vrac M, Chevallier F (2010) Autoregressive models for maxima and their applications to CH4 and N2O. Environmetrics 21: 189-207.
91. Vilar JA, Alonso AM, Vilar JM (2010) Non-linear time series clustering based on non-parametric forecast densities. Comput Stat Data Anal 54: 2850-2865.
92. Vilar JM, Vilar JA, Pértega S (2009) Classifying time series data: a nonparametric approach. J Classif 26(1): 3-28.
93. Wang N, Blostein S (2004) Adaptive zero-padding OFDM over frequency-selective multipath channels. Journal on Applied Signal Processing 10: 1478-1488.
94. Wedel M, Kamakura WA (1998) Market segmentation: Conceptual and methodological foundations. Kluwer, Boston.
95. 2. 5 ambient aerosol chemical databases. Environ Sci Technol 32: 3926-3934.
96. Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13: 841-847.
97. Zadeh LA (1965) Fuzzy sets. Inf Control 8: 338-353.
98. Zarandi MHF, Alaeddini A (2010) A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts. Inf Sci 180: 3033-3044.
99. Zeng Y, Garcia-Frias J (2006) A novel HMM-based clustering algorithm for the analysis of gene expression time-course data. Comput Stat Data Anal 50(9): 2472-2494.
100. Zhang Y, Wang W, Zhang W, Li Y (2008) A cluster validity index for fuzzy clustering. Inf Sci 178: 1205-1218.
101. Zhou Q, Huang GH, Chan CW (2004) Development of an intelligent decision support system for air pollution control at coal-fired power plants. Exp Syst Appl 26: 335-356.