[Machine Learning] clustering, again

I had posted few blogs about clustering:

K-means + heatmap

http://onetipperday.blogspot.com/2011/10/k-mean-clustering-heatmap.html

Clustering in R

http://onetipperday.blogspot.com/2012/04/clustering-analysis-2.html

SOM (Self-organizing map)

http://onetipperday.blogspot.com/2012/07/self-organizing-map-in-r.html

DTW (Dynamic Time Warping)

http://onetipperday.blogspot.com/2011/09/dtw-dynamic-time-warping.html

However, still feel not confident to some/many concepts in clustering. Following the study of Manduca Sexta, I will read more, including the following two references:

Different distance functions:

http://gedas.bizhat.com/dist.htm

The C Clustering Library provided by Michiel de Hoon et al. (the authors of Cluster)

http://bonsai.hgc.jp/~mdehoon/software/cluster/cluster.pdf

The expect output will be a more detailed post in clustering, esp. how to select the right distance functions and clustering methods according to different data type and questions. 

To be continued...