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In this paper we demonstrate a new density based clustering technique, CODSAS, for online clustering of streaming data into arbitrary shaped clusters. CODAS is a two stage process using a simple local density to initiate micro-clusters which are then combined into clusters. Memory efficiency is gained by not storing or re-using any data. Computational efficiency is gained by using hyper-spherical...
Fingerprint matching is adopted by a large family of indoor localization schemes, where collecting fingerprints is inevitable but all consuming. Recent studies show that the crowdsourcing scheme could be utilized to perform fingerprints collecting with desirable performance price ratio; however, since the fingerprints are collected from various mobile devices with crowdsourcing, it is difficult to...
Current studies of type-2 neural fuzzy systems (FSs) (NFSs) primarily focus on building a fuzzy model with high accuracy and disregard the interpretability of fuzzy rules. This paper proposes a data-driven interval type-2 (IT2) NFS with improved model interpretability (DIT2NFS-IP). The DIT2NFS-IP uses IT2 fuzzy sets in its antecedent part and intervals in its zero-order Takagi–Sugeno–Kang-type consequent...
A codebook design method for Hidden Markov Model (HMM) by using a Centroid Neural Network (CNN) is applied to a Korean monophone recognition problem in this paper. In order to alleviate the accuracy degradation problem in tied mixture HMM (TMHMM), this paper utilizes a clustering algorithm, called Centroid Neural Network with State Dependence measure (CNN(SD)), for TMHMMs. The CNN(SD) uses a novel...
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