1971/12/01 by William Rand · 3 citations
Computer Science · Mathematics · #Advanced Clustering Algorithms Research #Face and Expression Recognition #Bayesian Methods and Mixture Models #Cluster analysis #Data mining #Computer science #Measure (data warehouse) #Similarity (geometry) #Resampling #Data set #Stability (learning theory) #Set (abstract data type) #Consensus clustering #Similarity measure #Sensitivity (control systems) #Interpretation (philosophy) #Mathematics #Fuzzy clustering #Artificial intelligence #Machine learning #CURE data clustering algorithm
paper · doi:10.2307/2284239
openalex publication_date 1971/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Many intuitively appealing methods have been suggested for clustering data, however, interpretation of their results has been hindered by the lack of objective criteria. This article proposes several criteria which isolate specific aspects of the performance of a method, such as its retrieval of inherent structure, its sensitivity to resampling and the stability of its results in the light of new data. These criteria depend on a measure of similarity between two different clusterings of the same set of data; the measure essentially considers how each pair of data points is assigned in each clustering.