We present a non-greedy version of the recently published Principal Direction Divisive Partitioning (PDDP) algorithm. The PDDP algorithm creates a hierarchical taxonomy of a data set by successively splitting the data into sub-clusters. At each level the cluster with largest variance is split by a hyper-plane orthogonal to its leading principal component. The PDDP algorithm is known to produce high quality clusters, especially when applied to high dimensional data, such as document-word feature matrices. It also scales well with both the size and the dimensionality of the data set. However, at each level only the locally optimal choice of spitting is considered. At a later stage this often leads to a non-optimal global partitioning of the data. The non-greedy version of the PDDP algorithm (NGPDDP) presented in this paper address this problem. At each level multiple alternative splitting strategies are considered. Results from applying the algorithm to generated and real data (feature vectors from sets of text documents) are presented. The results show substantial improvements in the cluster quality.

Some insight into the behavior of adaptive filtering systems may be gained by comparing them with similar ranked-output retrieval systems. This is not easy however, a new optimization measure, intr...

Adaptive filtering of news is an area of information retrieval gaining substantial interest as services become more available on the Internet. This paper reports on a number of experiments involving a two-level clustering approach using a variety of techniques including threshold adaptation, topic vocabulary adaptation and both noun phrase and named entity adaptation. Our goal in this exploratory research is to empirically compare alternative configurations of our filtering approach that will allow us to better understand the relative value of the component subsystems.

In this paper we look at some of the problems in interacting with best-match retrieval systems. In particular, we examine the areas of interaction, some investigations of the complexity and breadth of interaction and attempts to categorise user's information seeking behaviour. We suggest that one of the difficulties of traditional IR systems in supporting information seeking is the way the information content of documents is represented. We discuss an alternative representation, based on how information is used within documents.

Past research has identified many different types of relevance in information retrieval (IR). So far, however, most evaluation of IR systems has been through batch experiments conducted with test c...