In this paper we discuss an interesting and useful property of clickstream data. Often a visit includes repeated views of the same page. We show that in three real datasets, sampled from the websites of technology and consulting groups and a news broadcaster, page repetitions occur for the majority as a very specific structure, namely in the form of nested palindromes. This can be explained by the widespread use of features which are available in any web browser: the "refresh" and "back" buttons. Among the types of patterns which can be mined from sequence data, many either stumble if symbol repetitions are involved, or else fail to capture interesting aspects related to symbol repetitions. In an attempt to remedy this, we characterize the palindromic structures, and discuss possible ways of making use of them. One way is to pre-process the sequence data by explicitly inserting these structures, in order to obtain a richer output from conventional mining algorithms. Another application we discuss is to use the information directly, in order to analyze certain aspects of the website under study. We also provide the simple linear-time algorithm which we developed to identify and extract the structures from our data.

Mining web traffic data has been addressed in literature mostly using sequential pattern mining techniques. Recently, a more powerful pattern called partial order was introduced, with the hope of providing a more compact result set. A further approach towards this goal, valid for both sequential patterns and partial orders, consists in building a statistical significance test for frequent patterns. Our method is based on probabilistic generative models and provides a direct way to rank the extracted patterns. It leaves open the number of patterns of interest, which depends on the application, but provides an alternative criterion to frequency of occurrence: statistical significance. In this paper, we focus on the construction of an algorithm which calculates the probability of partial orders under a first-order Markov reference model, and we show how to use those probabilities to assess the statistical significance of a set of mined partial orders.

In this paper, we propose the use of lustering methods for automatic counting of pedestrians in video sequences. As input, we consider the output of those detection/tracking systems that overestimate the number of targets. Clustering techniques are applied to the resulting trajectories in order to reduce the bias between the number of tracks and the real number of targets. The main hypothesis is that those trajectories belonging to the same human body are more similar than trajectories belonging to different individuals. Several data representations and different distance/similarity measures are proposed and compared, under a common hierarchical clustering framework, and both quantitative and qualitative results are presented

In this paper we deal with the multiobject tracking problem in the particular case of pedestrians, assuming the detection step already done. We use a Bayesian framework to combine the likelihood term provided by an image correlation algorithm with a prior distribution given by a discrete choice model for pedestrian behavior, calibrated on real data. We aim to show how the combination of the image information with a model of pedestrian behavior can provides appreciable results in real and complex scenarios.