  Bibliometrics such as the number of papers and times cited are often used to
compare researchers based on specific criteria. The criteria, however, are
different in each research domain and are set by empirical laws. Moreover,
there are arguments, such that the simple sum of metric values works to the
advantage of elders. Therefore, this paper attempts to constitute features from
time series data of bibliometrics, and then classify the researchers according
to the features. In detail, time series patterns are extracted from
bibliographic data sets, and then a model to classify whether the researchers
are "distinguished" or not is created by a machine learning technique. The
experiments achieved an F-measure of 80.0% in the classification of 114
researchers in two research domains based on the data sets of Japan Science and
Technology Agency and Elsevier's Scopus. In the future, we will conduct
verification on a number of researchers in several domains, and then make use
of discovering "distinguished" researchers, who are not widely known.
