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For clustering accuracy, on influx of data, the parameter-free incremental clustering research is essential. The sole purpose of this bibliometric analysis is to understand the reach and utility of incremental clustering algorithms. This paper shows incremental clustering for time series dataset was first explored in 2000 and continued thereafter till date. This Bibliometric analysis is done using Scopus, Google Scholar, Research Gate, and the tools like Gephi, Table2Net, and GPS Visualizer etc. The survey revealed that maximum publications of incremental clustering algorithms are from conference and journals, affiliated to Computer Science, Chinese lead publications followed by India then United States. Convergence optimality is another prominent keyword and less attentiveness towards correlation has observed. For betweenness and friendly measures keywords, after physics and astronomy; engineering is the contributing subject area, minimal contribution of review papers are observed in this art-search. The effectual incremental learning is feasible via parameter-free incremental clustering algorithm, applicable to all domains and hence this study.