Attending KDD 2008; Top 10 DM Algorithms & 10 challenging problems

So its final now, I'll be attending the KDD 2008 conference, 24-27 August in Las Vegas. As it says on SIGKDD website, I expect "awesomeness"!.

 "The annual ACM SIGKDD conference is the premier international forum for data mining
researchers and practitioners from academia, industry, and government to share their
ideas, research results and experiences. KDD-08 will feature keynote presentations,
oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits,
demonstrations, and the KDD Cup competition"

This brings this post to top 10's; the Top 10 DM Algorithms and 10 challenging problems in data mining. IEEE International Conference on Data Mining (ICDM) did some polls to identify 10 challenging problems and 10 most influential
algorithms in data mining. Results are avaialble on the following links.

The top 10 algorithms are as follows: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. and the paper describing them (highly recommended reading) is

The 10 challenging problems are as follows (they aren't TOP 10 problems, just problems)

    * Developing a Unifying Theory of Data Mining
    * Scaling Up for High Dimensional Data and High Speed Data Streams
    * Mining Sequence Data and Time Series Data
    * Mining Complex Knowledge from Complex Data
    * Data Mining in a Network Setting
    * Distributed Data Mining and Mining Multi-agent Data
    * Data Mining for Biological and Environmental Problems
    * Data-Mining-Process Related Problems
    * Security, Privacy and Data Integrity
    * Dealing with Non-static, Unbalanced and Cost-sensitive Data

Speaking of unable to find research problems, here you go; now go and work on your idea paper, it's not going to write itself.