Incremental Clustering Based on AutoEncoder

The rapid growth of data result in a lot of problems such as the data have too many features, the lack of storage space etc. This paper propose a new incremental clustering algorithm based on AutoEncoder. Firstly, the AutoEncoder are used to learn the features of the data, integrate the low-dimensional feature, and get the reduced representation from the raw data. Then run incremental clustering on the new data base on the original clustering results by reading the data once and dynamically update clustering centers. Experimental results show that the proposed algorithm can obtain a comparable clustering performance with k-means algorithm (Kmeans) and fuzzy c-means algorithm (FCM) on the four data set in UCI database. Meanwhile, the time consumption of the proposed algorithm is low, it can achieve incremental clustering and identify the outliers for the increasing data set effectively.