Data Mining_ Concepts and Techniques - Jiawei Han [396]
[KH95] Koperski, K.; Han, J., Discovery of spatial association rules in geographic information databases, In: Proc. 1995 Int. Symp. Large Spatial Databases (SSD’95) Portland, ME. (Aug. 1995), pp. 47–66.
[KH97] Kononenko, I.; Hong, S.J., Attribute selection for modeling, Future Generation Computer Systems 13 (1997) 181–195.
[KH09] Kim, M.-S.; Han, J., A particle-and-density based evolutionary clustering method for dynamic networks, In: Proc. 2009 Int. Conf. Very Large Data Bases (VLDB’09) Lyon, France. (Aug. 2009).
[KHC97] Kamber, M.; Han, J.; Chiang, J.Y., Metarule-guided mining of multi-dimensional association rules using data cubes, In: Proc. 1997 Int. Conf. Knowledge Discovery and Data Mining (KDD’97) Newport Beach, CA. (Aug. 1997), pp. 207–210.
[KHK99] Karypis, G.; Han, E.-H.; Kumar, V., CHAMELEON: A hierarchical clustering algorithm using dynamic modeling, COMPUTER 32 (1999) 68–75.
[KHY+08] Kargupta, H.; Han, J.; Yu, P.S.; Motwani, R.; Kumar, V., Next Generation of Data Mining. (2008) Chapman & Hall/CRC .
[KJ97] Kohavi, R.; John, G.H., Wrappers for feature subset selection, Artificial Intelligence 97 (1997) 273–324.
[KJSY04] Kargupta, H.; Joshi, A.; Sivakumar, K.; Yesha, Y., Data Mining: Next Generation Challenges and Future Directions. (2004) AAAI/MIT Press, Cambridge, MA .
[KK01] Kuramochi, M.; Karypis, G., Frequent subgraph discovery, In: Proc. 2001 Int. Conf. Data Mining (ICDM’01) San Jose, CA. (Nov. 2001), pp. 313–320.
[KKW+10] Kim, H.S.; Kim, S.; Weninger, T.; Han, J.; Abdelzaher, T., NDPMine: Efficiently mining discriminative numerical features for pattern-based classification, In: Proc. 2010 European Conf. Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD’10) Barcelona, Spain. (Sept. 2010).
[KKZ09] Kriegel, H.-P.; Kroeger, P.; Zimek, A., Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering, ACM Trans. Knowledge Discovery from Data (TKDD) 3 (1) (2009) 1–58.
[KLA+08] Khan, M.; Le, H.; Ahmadi, H.; Abdelzaher, T.; Han, J., DustMiner: Troubleshooting interactive complexity bugs in sensor networks, In: Proc. 2008 ACM Int. Conf. Embedded Networked Sensor Systems (SenSys’08) Raleigh, NC. (Nov. 2008), pp. 99–112.
[Kle99] Kleinberg, J.M., Authoritative sources in a hyperlinked environment, J. ACM 46 (1999) 604–632.
[KLV+98] Kennedy, R.L.; Lee, Y.; Van Roy, B.; Reed, C.D.; Lippman, R.P., Solving Data Mining Problems Through Pattern Recognition. (1998) Prentice-Hall .
[KM90] Kodratoff, Y.; Michalski, R.S., Machine Learning, An Artificial Intelligence Approach. (1990) Morgan Kaufmann ; Vol. 3.
[KM94] Kivinen, J.; Mannila, H., The power of sampling in knowledge discovery, In: Proc. 13th ACM Symp. Principles of Database Systems Minneapolis, MN. (May 1994), pp. 77–85.
[KMN+02] Kanungo, T.; Mount, D.M.; Netanyahu, N.S.; Piatko, C.D.; Silverman, R.; Wu, A.Y., An efficient k-means clustering algorithm: Analysis and implementation, IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI) 24 (2002) 881–892.
[KMR+94] Klemettinen, M.; Mannila, H.; Ronkainen, P.; Toivonen, H.; Verkamo, A.I., Finding interesting rules from large sets of discovered association rules, In: Proc. 3rd Int. Conf. Information and Knowledge Management Gaithersburg, MD. (Nov. 1994), pp. 401–408.
[KMS03] Kubica, J.; Moore, A.; Schneider, J., Tractable group detection on large link data sets, In: Proc. 2003 Int. Conf. Data Mining (ICDM’03) Melbourne, FL. (Nov. 2003), pp. 573–576.
[KN97] Knorr, E.; Ng, R., A unified notion of outliers: Properties and computation, In: Proc. 1997 Int. Conf. Knowledge Discovery and Data Mining (KDD’97) Newport Beach, CA. (Aug. 1997), pp. 219–222.
[KNNL04] Kutner, M.H.; Nachtsheim, C.J.; Neter, J.; Li, W., Applied Linear Statistical Models with Student CD. (2004) Irwin .
[KNT00] Knorr, E.M.; Ng, R.T.; Tucakov, V., Distance-based outliers: Algorithms and applications, The VLDB J. 8 (2000) 237–253.
[Koh95] Kohavi, R., A study of cross-validation and bootstrap