• Choose a category
• All
• Classic-Fiction

[LM98b] In: (Editors: Liu, H.; Motoda, H.) Feature Extraction, Construction, and Selection: A Data Mining Perspective (1998) Kluwer Academic.

[LNHP99] Lakshmanan, L.V.S.; Ng, R.; Han, J.; Pang, A., Optimization of constrained frequent set queries with 2-variable constraints, In: Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’99) Philadelphia, PA. (June 1999), pp. 157–168.

[L-NK03] Liben-Nowell, D.; Kleinberg, J., The link prediction problem for social networks, In: Proc. 2003 Int. Conf. Information and Knowledge Management (CIKM’03) New Orleans, LA. (Nov. 2003), pp. 556–559.

[Los01] Loshin, D., Enterprise Knowledge Management: The Data Quality Approach. (2001) Morgan Kaufmann .

[LP97] Lenarcik, A.; Piasta, Z., Probabilistic rough classifiers with mixture of discrete and continuous variables, In: (Editors: Lin, T.Y.; Cercone, N.) Rough Sets and Data Mining: Analysis for Imprecise Data (1997) Kluwer Academic, pp. 373–383.

[LPH02] Lakshmanan, L.V.S.; Pei, J.; Han, J., Quotient cube: How to summarize the semantics of a data cube, In: Proc. 2002 Int. Conf. Very Large Data Bases (VLDB’02) Hong Kong, China. (Aug. 2002), pp. 778–789.

[LPWH02] Liu, J.; Pan, Y.; Wang, K.; Han, J., Mining frequent itemsets by opportunistic projection, In: Proc. 2002 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD’02) Edmonton, Alberta, Canada. (July 2002), pp. 239–248.

[LPZ03] Lakshmanan, L.V.S.; Pei, J.; Zhao, Y., QC-Trees: An efficient summary structure for semantic OLAP, In: Proc. 2003 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’03) San Diego, CA. (June 2003), pp. 64–75.

[LS95] Liu, H.; Setiono, R., Chi2: Feature selection and discretization of numeric attributes, In: Proc. 1995 IEEE Int. Conf. Tools with AI (ICTAI’95) Washington, DC. (Nov. 1995), pp. 388–391.

[LS97] Loh, W.Y.; Shih, Y.S., Split selection methods for classification trees, Statistica Sinica 7 (1997) 815–840.

[LSBZ87] Langley, P.; Simon, H.A.; Bradshaw, G.L.; Zytkow, J.M., Scientific Discovery: Computational Explorations of the Creative Processes. (1987) MIT Press, Cambridge, MA .

[LSL95] Lu, H.; Setiono, R.; Liu, H., Neurorule: A connectionist approach to data mining, In: Proc. 1995 Int. Conf. Very Large Data Bases (VLDB’95) Zurich, Switzerland. (Sept. 1995), pp. 478–489.

[LSW97] Lent, B.; Swami, A.; Widom, J., Clustering association rules, In: Proc. 1997 Int. Conf. Data Engineering (ICDE’97) Birmingham, England. (Apr. 1997), pp. 220–231.

[Lux07] Luxburg, U., A tutorial on spectral clustering, Statistics and Computing 17 (2007) 395–416.

[LV88] Loh, W.Y.; Vanichsetakul, N., Tree-structured classificaiton via generalized discriminant analysis, J. American Statistical Association 83 (1988) 715–728.

[LZ05] Li, Z.; Zhou, Y., PR-Miner: Automatically extracting implicit programming rules and detecting violations in large software code, In: Proc. 2005 ACM SIGSOFT Symp. Foundations of Software Engineering (FSE’05) Lisbon, Portugal. (Sept. 2005).

[MA03] Mitra, S.; Acharya, T., Data Mining: Multimedia, Soft Computing, and Bioinformatics. (2003) John Wiley & Sons .

[MAE05] Metwally, A.; Agrawal, D.; El Abbadi, A., Efficient computation of frequent and top-k elements in data streams, In: Proc. 2005 Int. Conf. Database Theory (ICDT’05) Edinburgh, Scotland. (Jan. 2005), pp. 398–412.

[Mac67] MacQueen, J., Some methods for classification and analysis of multivariate observations, Berkeley, CA. Proc. 5th Berkeley Symp. Math. Stat. Prob. 1 (1967) 281–297.

[Mag94] Magidson, J., The CHAID approach to segmentation modeling: CHI-squared automatic interaction detection, In: (Editor: Bagozzi, R.P.) Advanced Methods of Marketing Research (1994) Blackwell Business, pp. 118–159.

[Man00] Mannila, H., Theoretical frameworks of data mining, SIGKDD Explorations 1 (2000) 30–32.

[MAR96] Mehta, M.; Agrawal, R.; Rissanen, J., SLIQ: A fast scalable classifier for data mining, In: Proc. 1996 Int. Conf. Extending Database Technology (EDBT’96) Avignon, France. (Mar. 1996), pp. 18–32.

[Mar09] Marsland,