On data mining, compression, and Kolmogorov complexity

Data Mining and Knowledge Discovery

Volumn 15, Issue 1, 2007, Pages 3-20

  Faloutsos, Christos ^a   Megalooikonomou, Vasileios ^b  

^a Carnegie Mellon University   (United States)

^b Temple University   (United States)

Author keywords

Classification; Clustering; Compression; Data mining; Forecasting; Kolmogorov complexity; Outliers

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATA COMPRESSION; DATABASE SYSTEMS; FORECASTING;

DATASETS; KOLMOGOROV COMPLEXITY; OUTLIERS;

DATA MINING;

EID: 34547284903     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-006-0057-3     Document Type: Article

References (36)

1. Barnsley M (1988). Fractals everywhere. Academic Press Inc, San Diego, CA
2. Barnsley MF, Sloan AD (1988) A better way to compress images. Byte, January 1988. pp 215-223
3. Bentley JL, Sleator DD, Tarjan RE, Wei VK (1986) A locally adaptive data compression scheme. CACM, 29(4):320-330
4. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth Inc., Belmont, CA, USA
5. Chakrabarti D, Papadimitriou S, Modha DS, Faloutsos C (2004) Fully automatic cross-associations. In: KDD Conference, Seattle, WA, pp 79-88
6. Codd EF (1971) A database sublanguage founded on the relational calculus. In: SIGFIDET Workshop, pp 35-68
7. Cover TM, Thomas JA (1991) Elements of information theory. John Wiley and Sons
8. Damerau FJ (1964) A technique for computer detection and correction of spelling errors. CACM, March 1964. 7(3):171-176
9. Elias P (1975) Universal codeword sets and representations of integers. IEEE Trans Inf Theory IT-21:194-203
10. Eschera MA, Fu KS (1984) A graph distance measure for image analysis. IEEE Trans Syst Man Cybern 14:398-407
11. Gersho A, Gray RM (1992) Vector quantization and signal compression. Kluwer Academic Publishers
12. Hamerly G, Elkan C (2003) Learning the k in k-means. In: Proceedings of the NIPS
13. Keogh E (2002) Exact indexing of dynamic time warping. Int. conf. on very large data bases, 406-417
14. Keogh EJ, Lonardi S, Ratanamahatana C(Ann) (2004) Towards parameter-free data mining. In: KDD Conference, Seattle, WA, pp 206-215
15. Kumaraswamy K, Megalooikonomou V, Faloutsos C (2004) Fractal dimension and vector quantization. Inform Process Lett 91(3):107-113
16. Li M, Badger JH, Chen X, Kwong S, Kearney P, Zhang H (2001) An information-based sequence distance and its application to whole mitochondrial genome phylogeny. Bioinformatics, 17(2):149-154
17. Li M, Chen X, Li X, Ma B, Vitanyi P (2003) The similarity metric. In: Proceedings of the fourteenth annual ACM-SIAM symposium on discrete algorithms, pp 863-872
18. Li M, Vitanyi P (1997) An Introduction to Kolmogorov complexity and its applications, 2nd edn. Springer Verlag, New York
19. Mackenzie D (1999) New clues to why size equals destiny. Science, 284(5420):1607-1609; 10.1126/science.284.5420.1607
20. Megalooikonomou V (1997) Kolmogorov incompressibility method in formal proofs: a critical survey. Technical Report TR CS-97-01, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
21. Megalooikonomou V, Kontos D, Danglemaier J, Javadi A, Bakic PA, Maidment ADA (2006) A representation and classification scheme for tree-like structures in medical images: An application on branching pattern analysis of ductal trees in x-ray galactograms. In: Proceedings of the SPIE conference on medical imaging, San Diego, CA, vol 6144, March 2006, pp 497-505
22. Megalooikonomou V, Wang Q, Li G, Faloutsos C, (2005) A Multiresolution Symbolic Representation of Time Series. In: Proceedings of the 21st IEEE international conference on data engineering (ICDE05), Tokyo, Japan, Apr. 2005, pp 668-679
23. Mehta M, Agrawal R, and Rissanen J (1996) SLIQ: a fast scalable classifier for data mining. In: Proceedings of the 1996 Int. Conference on Extending Database Technology (EDBT'96), March 1996. Avignon, France, pp 18-32
24. Mitchell T (1997) Machine Learning. McGraw Hill
25. Martin-Löf P (1966) The definition of random sequences. Inform Contr 9:602-619
26. Papadimitriou S, Kitagawa H, Gibbons P, Faloutsos C (2003) LOCI: Fast outlier detection using the local correlation integral. ICDE, March 2003, pp 315-326
27. Pelleg D, Moore AW (2000) X-means: extending k-means with efficient estimation of the number of clusters. In: ICML '00: Proceedings of the seventeenth international conference on machine learning, San Francisco, CA, USA, 2000. Morgan Kaufmann Publishers Inc, pp 727-734
28. Quinlan J (1993) C4.5: Programs for machine learning. Morgan Kauffman
29. Rabiner L, Juang B-H (1993) Fundamentals of speech recognition. Prentice Hall
30. Ramakrishnan R, Gehrke J (2002) Database management systems, 3rd edn. McGraw-Hill
31. Schroeder M (1991) Fractals, chaos, power laws: minutes from an infinite paradise. W.H. Freeman and Company, New York
32. Yi B-K, Jagadish HV, Faloutsos C (1998) Efficient retrieval of similar time sequences under time warping. ICDE 98, Feb. 23-27
33. Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. ACM SIGMOD, may 1996, pp 103-114
34. Zhu L, Rao A, Zhang A (2002) Theory of keyblock-based image retrieval. ACM Trans Inform Syst 20(2):224-257
35. Ziv J, Lempel A (1978) Compression of individual sequences via variable-rate encoding. IEEE Trans Inform Theory IT-24:530-536
36. Zobel J, Moffat A, Sacks-Davis R (1992) An efficient indexing technique for full-text database systems. VLDB, Aug. 23-27, pp 352-362