Proximity problems are typically studied for data represented as points in the Euclidean space. In this talk I will investigate instances of proximity problems for data that are more complex than Euclidean points. First, I will describe a hashing scheme for efficient indexing of data represented as sets. I will show how this scheme can be applied to provide a scalable solution to the problem of retrieving web pages similar to a query page. Also I will present a technique for automatic evaluation of web page representation strategies.
In the second part of the talk I will address the problem of clustering temporal data. I will motivate the problem with an application in the area of genomic data analysis, and I will discuss the connection of temporal lustering with the problem of segmenting sequences and with traditional clustering.