  Clustering has been widely applied to Information Retrieval (IR) on the
grounds of its potential improved effectiveness over inverted file search.
Clustering is a mostly unsupervised procedure and the majority of the
clustering algorithms depend on certain assumptions in order to define the
subgroups present in a data set .A clustering quality measure is a function
that, given a data set and its partition into clusters, returns a non-negative
real number representing the quality of that clustering. Moreover, they may
behave in a different way depending on the features of the data set and their
input parameters values. Therefore, in most applications the resulting
clustering scheme requires some sort of evaluation as regards its validity. The
quality of clustering can be enhanced by using a Cellular Automata Classifier
for information retrieval. In this study we take the view that if cellular
automata with clustering is applied to search results (query-specific
clustering), then it has the potential to increase the retrieval effectiveness
compared both to that of static clustering and of conventional inverted file
search. We conducted a number of experiments using ten document collections and
eight hierarchic clustering methods. Our results show that the effectiveness of
query-specific clustering with cellular automata is indeed higher and suggest
that there is scope for its application to IR.
