Conceptual clustering forms groups of related data items using some distance metrics. Inductive techniques like attribute-oriented induction AOI) generate meta-level descriptions of attribute values without explicitly stated distance metrics and overall goodness functions required for a clustering algorithm. The generalisation process in AOI, per attribute basis, groups attribute values using concise descriptions of a tree hierarchy for that attribute. A conceptual clustering approach is considered for attribute-oriented induction where goodness functions for maintaining intra-cluster tightness within clusters, inter-cluster dissimilarity between clusters and cluster quality evaluation are defined. Attributes are partitioned into natural common parent concept clusters, their tightness, dissimilarity and quality computed for determining a cluster to generalise within the chosen attribute. This principle minimises overgeneralisation and follows a natural clustering approach. Overall, AOI is presented as an agglomerative clustering algorithm, clusterAOI and comparative effectiveness with classical AOI analysed.