Mining statistically significant substrings based on the chi-square measure

Pattern Discovery Using Sequence Data Mining: Applications and Studies

Volumn , Issue , 2011, Pages 73-82

  Dutta, Sourav ^a   Bhattacharya, Arnab ^b  

^a IBM RESEARCH INDIA   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY KANPUR   (India)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84873144286     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-61350-056-9.ch004     Document Type: Chapter

References (11)

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4. Dutta, S., & Bhattacharya, A. (2010). Most significant substring mining based on chi-square measure. In Proc. of 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, (pp. 319-327).
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9. Read, T., & Cressie, N. (1989). Pearson's X2 and the likelihood ratio statistic G2: A comparative review. International Statistical Review, 57(1), 19-43. doi:10.2307/1403582
10. Regnier, M., & Vandenbogaert, M. (2006). Comparison of statistical significance criteria. Journal of Bioinformatics and Computational Biology, 4(2), 537-551. doi:10.1142/S0219720006002028
11. Ye, N., & Chen, Q. (2001)An anomaly detection technique based on chi-square statistics for detecting intrusions into information systems. Quality and Reliability Engineering International, 17(2), 105-112. doi:10.1002/qre.392