Novel Trends in Clustering - Evolving Application Domains of Data Warehousing and Mining - page 207

obtain good results. Therefore, semi-supervised
clustering provides the potential to combine the
cognitive abilities of humans with the computing
power of machines for clustering.
The third question is mainly addressed by ap-
proaches to parameter-free clustering. To be most
informative to the user, the clustering result must
have a suitable level of abstraction. The cluster-
ing should concisely summarize the important
characteristics of the data without over fitting.
For most clustering algorithms the resolution of
the result depends on input parameters which are
difficult to estimate.Approaches to parameter-free
clustering automatically select a suitable level of
abstraction by introducing ideas from information
theory into clustering.
There are many challenges for clustering in
the future, which cannot all be mentioned here.
Definitely, there is a strong need for highly scalable
techniques and for techniques which can combine
data originating from different sources. The devel-
opment of novel techniques will be promoted by
the needs of novel applications. Clustering is an
important step on the path from data to knowledge
and will therefore continue attracting the attention
of generations of researchers to come.

Achtert, E., Böhm, C., Kriegel, H.-P., Kröger, P.,
Müller-Gorman, I., & Zimek,A. (2007). Detection
and Visualization of Subspace Cluster Hierarchies.
In Proc. of International Conference on Database
Systems for Advanced Applications (DASFAA )
(pp. 152-163).

Achtert, E., Kriegel, H.-P., Pryakhin, A., & Schu-
bert, M. (2006). Clustering Multi-represented
Objects Using Combination Trees. In Proc. of
Pacific-Asia Conference on Knowledge Discovery
and Data Mining (PAKDD) (pp. 174-178).

Aggarwal, C. C. (2007). On Density Based Trans-
forms for Uncertain Data Mining. In Proc. of IEEE
International Conference on Data Engineering
(ICDE) (pp. 866-875).

Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S.
(2003).A Framework for Clustering Evolving Data
Streams. In Proc. of International Conference on
Very Large Data Bases (VLDB) (pp.81-92).

Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S.
(2004). A Framework for Projected Clustering
of High Dimensional Data Streams. In Proc. of
International Conference on Very Large Data
Bases (VLDB) (pp.852-863).

Aggarwal, C. C., Wolf, J. L., Yu, P. S., Procopiuc,
C., & Park, J. S. (1999). Fast algorithms for
projected clustering. In Proc. of ACM SIGMOD
International Conference on Management of Data
(SIGMOD) (pp. 61-72).

referenceS

Achtert, E., Böhm, C., David, J., Kröger, P., &
Zimek,A. (2008). Robust Clustering inArbitrarily
Oriented Subspaces. In Proc. of SIAM Interna-
tional Conference on Data Mining (SDM) (pp.
763-774).

Aggarwal, C. C., & Yu, P. S. (2000). Finding Gen-
eralized Projected Clusters in High Dimensional
Space. In Proc. of ACM SIGMOD International
Conference on Management of Data (SIGMOD)
(pp. 70-81).

Achtert, E., Böhm, C., Kriegel, H.-P., Kröger, P.,
Müller-Gorman, I., & Zimek, A. (2006). Finding
Hierarchies of Subspace Clusters. In Proc. of Eu-
ropean Conference on Principles and Practice of
Knowledge Discovery (PKDD) (pp. 446-453).

Aggarwal, C. C., & Yu, P. S. (2008). A Frame-
work for Clustering Uncertain Data Streams. In
Proc. of IEEE International Conference on Data
Engineering (ICDE) (pp. 150-159).