Cluster analysis has long been a fundamental task in data mining and machine learning. However, traditional clustering methods concentrate on producing a single solution, even though multiple alternative clusterings may exist. It is thus difficult for the user to validate whether the given solution is in fact appropriate, particularly for large and complex datasets. In this paper we explore the critical requirements for systematically finding a new clustering, given that an already known clustering is available and we also propose a novel algorithm, COALA, to discover this new clustering. Our approach is driven by two important factors; dissimilarity and quality. These are especially important for finding a new clustering which is highly informative about the underlying structure of data, but is at the same time distinctively different from the provided clustering. We undertake an experimental analysis and show that our method is able to outperform existing techniques, for both synthetic and real datasets.

Data clustering is a fundamental and very popular method of data analysis. Its subjective nature, however, means that different clustering algorithms or different parameter settings can produce widely varying and sometimes conflicting results. This has led to the use of clustering comparison measures to quantify the degree of similarity between alternative clusterings. Existing measures, though, can be limited in their ability to assess similarity and sometimes generate unintuitive results. They also cannot be applied to compare clusterings which contain different data points, an activity which is important for scenarios such as data stream analysis.In this paper, we introduce a new clustering similarity measure, known as ADCO, which aims to address some limitations of existing measures, by allowing greater flexibility of comparison via the use of density profiles to characterize a clustering. In particular, it adopts a 'data mining style' philosophy to clustering comparison, whereby two clusterings are considered to be more similar, if they are likely to give rise to similar types of prediction models.Furthermore, we show that this new measure can be applied as a highly effective objective function within a new algorithm, known as MAXIMUS, for generating alternate clusterings.

Abstract. The unsupervised nature of cluster analysis means that objects can be clustered in many different ways. This means that different clustering algorithms can lead to vastly different results. To address this, clustering similarity comparison methods have traditionally been used to quantify the degree of similarity between alternative clusterings. However, existing techniques utilize only the point-to-cluster memberships to calculate the similarity, which can lead to unintuitive results. They also can't be applied to analyze clusterings which only partially share points, which can be the case in stream clustering. In this paper we introduce a new measure named ADCO, which takes into account density profiles for each attribute and aims to address these problems.We provide experiments to demonstrate this new measure can often provide a more reasonable similarity comparison between different clusterings than existing methods.