Abstract
Extreme Learning Machine (ELM) is originally introduced for regression and classification. This paper extends ELM for clustering using Extreme Learning Machine Auto Encoder (ELM-AE) which learn key features of the input data. The embedding created by multiplying the input data with the output weights of ELM-AE is shown to produce better clustering results than clustering the original data space. Furthermore, ELM-AE is used to find the starting cluster points for k-means clustering, which produces better results than randomly assigning the cluster start points. The experimental results show that the proposed clustering algorithm Extreme Learning Machine Auto Encoder Clustering (ELM-AEC) is better than k-means clustering and is competitive with Unsupervised Extreme Learning Machine (USELM).
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