Clustering of document databases is useful for both browsing and searc
hing purposes; however, this can be a prohibitively expensive computat
ional process for lai ge collections. This problem is compounded when
the clustering structure must reflect a constantly changing database.
Therefore, efficient algorithms which maintain an existing clustering
structure are desirable, This study provides the details of a large-sc
ale implementation of the Cover-Coefficient-based Incremental Clusteri
ng Methodology (C(2)ICM). The experiments performed on a sample of the
MARIAN database show that its resource requirements are within practi
cal bounds for most platforms. Furthermore, C(2)ICM offers considerabl
e savings over reclustering. The results of this study will lead to an
additional type of browsing and/or searching facility on the Virginia
Tech-based MARIAN large online public access library catalog (OPAC) p
roject.