In this paper, we present an incremental clustering algorithm in the logical combinatorial approach to pattern recognition, which finds incrementally the β0-compact sets with radius α of an object collection. The proposed algorithm allows generating an intermediate subset of clusters between the β0-connected components and β0-compact sets (including both of them as particular cases). The evaluation experiments on standard document collections show that the proposed algorithm outperforms the algorithms that obtain the β0-connected components and the β0-compact sets.

There is presently no unified methodology that allows the evaluation of supervised and non-supervised classification algorithms. Supervised problems are evaluated through Quality Functions that require a previously known solution for the problem, while non-supervised problems are evaluated through several Structural Indexes that do not evaluate the classification algorithm by using the same pattern similarity criteria embedded in the classification algorithm. In both cases, a lot of useful information remains hidden or is not considered by the evaluation method, such as the quality of the supervision sample or the structural change generated by the classification algorithm on the sample. This paper proposes a unified methodology to evaluate classification problems of both kinds, that offers the possibility of making comparative evaluations and yields a larger amount of information to the evaluator about the quality of the initial sample, when it exists, and regarding the change produced by the classification algorithm.