As powerful as clickpath mining methods can be, they often lead to huge incomprehensible and non-interesting result sets. Our clickpath mining practice at MSN was faced with challenges of keeping analysts closer to the data exploration process, revealing powerful insight from clickpath mining that business owners can directly act upon. These challenges stressed the importance of an interactive and visual representation of clickpath mining results. Most products today that can perform clickpath visualization do so by presenting massive cross-weaving web graphs. We present a new type of clickpath visualization which focuses only on clickpaths of interest, simplifying the visualization space while still retaining the same degree of mineable knowledge in the data. We also describe visualization techniques we have used to enhance the detection of interesting clickpath patterns from data, and provide a real-life case study that has benefited from the use of our implemented clickpath visualizer PAVE.

Data mining research has long concentrated on the five main areas: clustering, association discovery, classification, forecasting and sequential patterns. Web data mining projects are concerned mainly with text mining, user segmentation, forecasting web usage and analyzing users' clickstream patterns. We present a new type of web usage mining called funnel analysis or funnel report mining. A funnel report is a study of the retention behavior among a series of pages or sites. For example, of all hits on the home page of www.msn.com, what percentages of those are followed by hits to moneycentral.msn.com? What percentage of www.msn.com hits are followed by moneycentral.msn.com, and then www.msnbc.com? What are the most interesting funnels starting with www.msn.com? Where does the greatest drop off rate occur after a user has hit MSNBC? Funnel reports are extremely useful in e-business because they give product planners an idea of how usable and well-structured their site is. From our experience performing web usage mining for the MSN network of sites, funnel reports are requested even more than user segmentation analyses, site affiliation studies and classification exercises. In this paper, we define a framework for funnel analysis and provide a tree-based solution we have been using successfully to extract all relevant funnels using only one scan of the data file.

Although there have been many studies on data mining, to date there have been few research prototypes or commercial systems supporting comprehensive query-driven mining, which encourages interactive exploration of the data. Our thesis is that constraint constructs and the optimization they induce play a pivotal role in mining queries, thus substantially enhancing the usefulness and performance of the mining system. This is based on the analogy of declarative query languages like SQL and query optimization which have made relational databases so successful. To this end, our proposed demo is not yet another data mining system, but of a new paradigm in data mining - mining with constraints, as the important first step towards supporting ad-hoc mining in DBMS.