Fuzzy Classification of Incomplete Data with Adaptive Volume

Abstract

For solving the incomplete data problem of missing feature values in prototype data, various strategies were proposed. In this paper, two improved approaches are proposed to estimate the missing values of incomplete data. The two approaches are based on combining the adaptive volume GustafsonKessel algorithm (GKA) and the nearest vector features under the distance norm evaluated by complete data. The GKA with adaptive volume is applied for clustering and classifying the results. At last, compared the result with other strategies, and the computer simulations show that the improved strategies provide superior effects.

Comparison of Algorithms for Clustering Incomplete Data

“…The Nearest Cluster Strategy (NCS) [25] uses the Gustafson-Kessel [12] clustering. First all data with missing values are removed (marginalisation), the cluster centres are calculated.…”