We present the MULICsoft software clustering tool. This tool is intended for categorical data sets in which each categorical attribute value (CA) has a 'weight' in the range 0.0 to 1.0, indicating how strongly the corresponding CA should influence the clustering process. MULICsoft produces as many clusters as there naturally exist in the data set. Each cluster consists of layers formed gradually through iterations, by reducing the similarity criterion for inserting objects in layers of a cluster at different iterations. We have applied this tool to clustering the mozilla software system. The objects in the data are files. The CAs represent the relationships, such as invocation and dependency relationships, between files. The weights depend on the number of times that each file invoked other files during a run time profiling of execution. The results of MULICsoft are better than those of LIMBO, BUNCH and ACDC, as shown by the MoJo error rates that are derived by comparing the computed partitions of mozilla with an authoritative manual partitioning.

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