Affinity propagation clustering for symbolic interval data based on mutual distances
适用于区间数据的基于相互距离的相似性传播聚类

Clustering for symbolic data is an important extension of conventional clustering, and interval representation for symbolic data is often used. The symmetrical measures in conventional clustering algorithms are sometimes not fit to interval data and the initialization is another severe problem that can affect the clustering algorithms. One metric called mutual distances for interval data was proposed; based on the metric, a new clustering method named affinity propagation clustering that could solve the problem initialization was used. Then, affinity propagation clustering for symbolic interval data based on mutual distance was given. Theoretical explanation and experiments indicate that the proposed algorithm outperforms K-means based on Euclidean distances for the interval symbolic data.