  In this paper, we employ Machine Learning algorithms on multi-mission
observations for the classification of accretion states of outbursting Black
hole X-ray binaries for the first time. Archival data from RXTE, Swift, MAXI
and AstroSat observatories are used to generate the hardness intensity diagrams
(HIDs) for outbursts of the sources XTE J1859+226 (1999 outburst), GX 339-4
(2002, 2004, 2007 and 2010 outbursts), IGR J17091-3624 (2016 outburst), and
MAXI J1535-571 (2017 outburst). Based on variation of X-ray flux, hardness
ratios, presence of various types of Quasi-periodic Oscillations (QPOs), photon
indices and disk temperature, we apply clustering algorithms like K-Means
clustering and Hierarchical clustering to classify the accretion states
(clusters) of each outburst. As multiple parameters are involved in the
classification process, we show that clustering algorithms club together the
observations of similar characteristics more efficiently than the `standard'
method of classification. We also infer that K-Means clustering provides more
reliable results than Hierarchical clustering. We demonstrate the importance of
the classification based on machine learning by comparing it with results from
`standard' classification.
