Begin to learn Lucene

Web and text mining is what I am interested but haven't touched much. Lucene is a good starting point since 1) easy basic idea to understand; 2) open-source code to study things happening behind; 3) search technology is hot and basic for more advanced text mining.

Excellent source about data mining jobs

Click here!

SUMM: Something to compete with Conjoint Analysis

SUMM vs. Conjoint Analysis

• SUMM can handle three or four times as many attributes as Conjoint because of its unique measurement scale.
• SUMM incorporates each respondent's subjective beliefs about vendors in each choice simulation, while Conjoint requires each product to be defined objectively.
• SUMM flows from a straightforward theory of how people make choices, so it is easier to understand how the numbers are generated.

You can find more about SUMM on Eric Marder Associates.

Powerful Conjoint Analysis

Conjoint analysis provides useful results for product development, pricing research, competitive positioning, and market segmentation. It also measures brand equity.

1st Educational Data Mining (EDM) (2008)

First International Conference on Educational Data Mining

Data Mining and Statistics in Service of Education

Call for papers (preliminary)

http://www.EducationalDataMining.org

June 20-21, 2008

Co-located with International Conference on Intelligent Tutoring Systems (ITS 2008)

The First International Conference on Educational Data Mining brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented educational software, as well as state databases of student test scores, has created large repositories of data reflecting how students learn. The EDM conference focuses on computational approaches for using those data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. This Conference emerges from preceding EDM workshops at the AAAI, AIED, ICALT, ITS, and UM conferences.

Topics of Interest

We welcome papers describing original work. Areas of interest include but are not limited to:

· Improving educational software. Many large educational data sets are generated by computer software. Can we use our discoveries to improve the software’s effectiveness?

· Domain representation. How do learners represent the domain? Does this representation shift as a result of instruction? Do different subpopulations represent the domain differently?

· Evaluating teaching interventions. Student learning data provides a powerful mechanism for determining which teaching actions are successful. How can we best use such data?

· Emotion, affect, and choice. The student’s level of interest and willingness to be a partner in the educational process is critical. Can we detect when students are bored and uninterested? What other affective states or student choices should we track?

· Integrating data mining and pedagogical theory. Data mining typically involves searching a large space of models. Can we use existing educational and psychological knowledge to better focus our search?

· Improving teacher support. What types of assessment information would help teachers? What types of instructional suggestions are both feasible to generate and would be welcomed by teachers?

· Replication studies. We are especially interested in papers that apply a previously used technique to a new domain, or that reanalyze an existing data set with a new technique.

Important Dates (tentative)

· Paper submissions: March 31, 2008

· Acceptance notification: April 30, 2008

· Camera ready paper: May 16, 2008

· Conference: June 20-21, 2008

Submission types:

· Full papers: Maximum of 10 pages. Should describe substantial, unpublished work.

· Young researcher: Maximum of 8 pages. Designed for graduate students and undergraduates.

Promote one site on the research on Conjoint Analysis

http://conjointonline.com/

Welcome to post more good resource here.

Thanks!

Spatial Database and Data Mining

Spatial data mining seems an emerging application, attracting more and more researchers. I find this group in UMN has interesting work, http://www.spatial.cs.umn.edu/, and you find some overall information from their survey article, http://www.spatial.cs.umn.edu/paper_ps/dmchap.pdf

Normalized Google Distance (NGD)

It is interesting to measure the two terms via information from Google. see paper

Paper accepted by Australian AI 2007

My paper, "Local Learning Algorithm for Markov Blanket Discovery: Correct, Data Efficient, Scalable and Fast", that co-authored with my professor Michel Desmarais is justed accepted as full paper by Australian National Conference on AI 2007. Keep working for more!!