  Citation maturity time varies for different articles. However, the impact of
all articles is measured in a fixed window. Clustering their citation
trajectories helps understand the knowledge diffusion process and reveals that
not all articles gain immediate success after publication. Moreover, clustering
trajectories is necessary for paper impact recommendation algorithms. It is a
challenging problem because citation time series exhibit significant
variability due to non linear and non stationary characteristics. Prior works
propose a set of arbitrary thresholds and a fixed rule based approach. All
methods are primarily parameter dependent. Consequently, it leads to
inconsistencies while defining similar trajectories and ambiguities regarding
their specific number. Most studies only capture extreme trajectories. Thus, a
generalised clustering framework is required. This paper proposes a feature
based multiple k means cluster ensemble framework. 1,95,783 and 41,732 well
cited articles from the Microsoft Academic Graph data are considered for
clustering short term (10 year) and long term (30 year) trajectories,
respectively. It has linear run time. Four distinct trajectories are obtained
Early Rise Rapid Decline (2.2%), Early Rise Slow Decline (45%), Delayed Rise No
Decline (53%), and Delayed Rise Slow Decline (0.8%). Individual trajectory
differences for two different spans are studied. Most papers exhibit Early Rise
Slow Decline and Delayed Rise No Decline patterns. The growth and decay times,
cumulative citation distribution, and peak characteristics of individual
trajectories are redefined empirically. A detailed comparative study reveals
our proposed methodology can detect all distinct trajectory classes.
