TY  - CONF
EP  - 663
VL  - 2
A1  - Muhamad Amin, A.H.
A1  - Khan, A.I.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867877970&doi=10.1109%2fICCISci.2012.6297111&partnerID=40&md5=d6ab2c21e9b43759246922a881a65aa1
SN  - 9781467319386
Y1  - 2012///
TI  - One-shot data clustering mechanism using a distributed associative memory scheme for on-site recognition within network of smart objects
ID  - scholars2837
SP  - 658
KW  - Classification technique; Comparative analysis; Computational networks; Data classification; Data clustering; Data sets; Distributed associative memories; Hierarchical graphs; Internet-of-Things; Interprocess communication; One-shot learning; Processing clusters; Recognition algorithm; Recognition mechanism; Recognition process; Seamless connectivity; Smart devices; Smart objects
KW  -  Associative processing; Associative storage; Clustering algorithms; Information science; Pattern recognition; Technology
KW  -  Classification (of information)
N1  - cited By 0; Conference of 2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012 ; Conference Date: 12 June 2012 Through 14 June 2012; Conference Code:93334
N2  - Reduced-Distributed Hierarchical Graph Neuron (R-DHGN) is a one-shot learning distributed associative memory algorithm for data classification, which reduces the computational complexity of existing recognition algorithms by distributing the recognition process into smaller processing clusters. This paper investigates an effect of unsupervised one-shot learning mechanism for data classification within a computational network. This computational network may represent a network of objects than can be deployed in the existing Internet-of-Things (IoT) environment that offers seamless connectivity between smart devices such as sensors. Our approach extends the pattern recognition capability of Distributed Hierarchical Graph Neuron (DHGN). The interprocess communications of DHGN scheme is significantly reduced, and preliminary results obtained from the series of comparative analyses with other established classifiers have indicated the capability of R-DHGN to produce one-shot classification technique using a lightweight recognition mechanism. Simple dataset of iris plants have been used to demonstrate such capability of R-DHGN. © 2012 IEEE.
AV  - none
CY  - Kuala Lumpur
ER  -