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WIM at TREE 2005* Junyo NIU, Lin Sun, Luzon Lou, Fang Deng, Chen Lin, Hailing Zheng, Nanjing Huang Lab of Web Information Mining, Computer Science & Engineering Department, Sudan University Shanghai

We have found that when we run a TREE-based classification task, we can run much faster with TREE-based model and find the same information, although TREE-based models are a bit harder to train. In the ad hoc task, for the first time, we have designed and implemented a classification algorithm. We used a very simple TREE to solve the task and obtained positive statistics as expected, and we also used it to solve another task we had to solve by hand. Finally, we have performed what we called a “Knowledge Discovery Track” for the EnterpriseTrack-known_item-task. We have presented the two major results of this project. First, for the enterprise track, we solved the ad hoc task, with TREE-based classification, without a single wrong guess on the known item search task. The second major result was that we can solve, directly using TREE, the knowledge discovery problem for the enterprise track. It's a relatively hard problem to solve. It requires the discovery and analysis of patterns in a long list of items. TREE is a general solution for this kind of problem, and we have discovered a few more general Trees to improve this general TREE to solve this general problem. The task we are working on is a very good choice for a knowledge discovery task. We solved the ad hoc problem without any errors on both the ad hoc task and the known item search task. The task we solved is very similar to one that was solved by two research groups in a previous year in the United States. It was solved by the group led by Y. Liu. The second major feature of these papers is that they describe a new Trees for the EnterpriseTrack-known_item task. This TREE is better than the current Trees. It is faster at solving tasks, and our results show that our approach is a better choice. 4. Trees, the Data Mining Challenge, and the Data Science Approaches The main event in the 2000s of Machine Learning was the rise in popularity of the term “Machine Learning”. Many Machine Learning approaches have also contributed to understanding the role of TEE in Machine Learning. I have noticed that many of those approaches used Trees to solve their problems and I wanted to discuss it with some participants in the contest to design the structure of TEE, as well as the reasons behind that choice. In 2000, the first Data Mining challenge was launched, the “Data Mining Challenge”.

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