Web-based Learning Solutions for Communities of Practice

REFERENCES
Ackerman, M. S. (1998). Augmenting organizational
memory: A field study of answer garden.
[TOIS]. ACM Transactions on Information Systems,
16(3), 203–224. doi:10.1145/290159.290160
Anderberg, M. R. (1973). Cluster analysis for
applications. New York: Academic Press, Inc.
Anthonisse, J. M. (1971). The rush in a directed
graph (Tech. Rep. BN 9/71). Amsterdam, The
Netherlands: Stichting Mathematisch Centrum.
Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E.,
Schubert, A., & Vicsek, T. (2002). Evolution of
the social network of scientific collaborations.
Physica A, 311(3-4), 590–614. doi:10.1016/
S0378-4371(02)00736-7
Berners-Lee, T. (2007). Giant global graph.
Bock, H. H. (1989). Probabilistic aspects in cluster
analysis. In Conceptual and numerical analysis of
data. Berlin, Germany: Springer-Verlag.
Burke, R. (2002). Hybrid recommender systems:
Survey and experiments. User Modeling
and User-Adapted Interaction, 12(4), 331–370.
doi:10.1023/A:1021240730564
Chakrabarti, S., Dom, B. E., Gibson, D., Kumar,
R., Raghavan, P., Rajagopalan, S., & Tomkins, A.
(1998). Spectral filtering for resource discovery.
In Proceedings of the ACM SIGIR workshop on
Hypertext Information Retrieval on the Web.
Ding, C., & He, X. (2002). Cluster merging and
splitting in hierarchical clustering algorithms. In
Proceedings of the IEEE International Conference
on Data Mining (ICDM’02).
Ding, C., He, X., Zha, H., Gu, M., & Simon, H.
(2001). A min-max cut algorithm for graph partitioning
and data clustering. In Proceedings of the
IEEE Int’l Conf. Data Mining (pp. 107-114).
92
Mining Unnoticed Knowledge in Collaboration Support Systems
Ester, M., Kriegel, H., Sander, J., & Xu, X. (1996).
A Density-Based Algorithm for Discovering
Clusters in Large Spatial Databases with Noise.
In Proceedings of 2 nd International Conference
on KDD.
Fink, J., & Kobsa, A. (2001). A review and analysis
of commercial user modeling servers for personalization
on the World Wide Web. User Modeling
and User-Adapted Interaction, Special Issue on
Deployed User Modeling, 10(3-4).
Freeman, L. C. (1977). A set of measures of
centrality based on betweenness. Sociometry, 40,
35–41. doi:10.2307/3033543
Guha, S., Rastogi, R., & Shim, K. (1998). CURE:
An efficient clustering algorithm for large databases.
In Proceedings of ACM SIGMOD International
Conference on Management of Data (pp.
73-84). New York: ACM Press.
Han, J., & Kamber, M. (2001). Data mining:
Concepts and techniques. San Francisco: Morgan
Kaufmann Publishers.
Hand, D. J., Mannila, H., & Smyth, P. (2000).
Principles of data mining. Cambridge, MA: MIT
Press.
Haythornthwaite, C., Kazmer, M. M., Robins,
J., & Shoemaker, S. (2000). Community development
among distance learners: Temporal and
technological dimensions. Journal of Computer-
Mediated Communication, 6(1), 1–26.
Jain, A. K., & Dubes, R. C. (1988). Algorithms
for clustering data. Upper Saddle River, NJ:
Prentice-Hall, Inc.
Johnson, S. C. (1967). Hierarchical clustering
schemes. Psychometrika, 2, 241–254. doi:10.1007/
BF02289588
Karypis, G., Han, E. H., & Kumar, V. (1999).
Chameleon: A hierarchical clustering algorithm
using dynamic modeling. IEEE Computer, 32,
68–75.