Analysis of Mined Knowledge

The output of knowledge mining algorithms is often not in a form suitable for direct human consumption, and hence there is a need to develop techniques and tools for helping an analyst better assimilate it. Following are some of the issues that need to be addressed in this area:

1. Usage Analysis Tools: There is a need to develop tools which incorporate statistical methods, visualization, and human factors to help better understand the mined knowledge. Section 4 provided a survey of the current literature in this area.

2. Interpretation of Mined Knowledge: One of the open issues in data mining, in general, and Web mining, in particular, is the creation of intelligent tools that can assist in the interpretation of mined knowledge. Clearly, these tools need to have specific knowledge about the particular problem domain to do any more than filtering based on statistical attributes of the discovered rules or patterns. In Web mining, for example, intelligent agents could be developed that based on discovered access patterns, the topology of the Web locality, and certain heuristics derived from user behavior models, could give recommendations about changing the physical link structure of a particular site. As a simple example, consider an agent that (among other things) looks at the difference between the visit frequency for a particular page and the number of frequent user paths ending in that page. This difference could be used to determine if the page constitutes an entry point. This may suggest the other navigational links should be placed on that page to increase traffic to other clusters of pages.