There are two different directions for the future works: one is to use machine learning techniques or background knowledge to improve the clustering pro-cess; the second one is to use the obtained hierarchy like an input for a post-processing algorithm to improve the information supplied to the user.
1 ) I plan to investigate much more deeply the relationship between clus-tering techniques and feature selection combining these two techniques. From our preliminary results we have seen that clustering (hierarchical one in this thesis) can guide the process of feature selection and also the feature selec-tion, used to select a subspace for distance computation helps the clustering approach to obtains very good results. Using background knowledge is very interesting because pushing into the hierarchy construction some background knowledge like a constraints defined by the user can improve the results of
7.2. FUTURE WORKS the whole process: and this procedure allows to personalize the structured results taking account of user needs. To do this, fortunately nowadays, a lot of useful resources are present on the web. For this reason, extracting back-ground knowledge and pushing it inside an unsupervised approach seems a promising goal.
2 ) From the other point of view, an interesting thing is to study a good way to label each cluster: in this way we can supply the user a good way to visualize and understand the data. Using the output of the proposed approach like an input, for instance using criteria like MDL and others, we can cut the hierarchy to obtain a good flat clustering.
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