Cognitive Computation (2019) 11:271–293 https://doi.org/10.1007/s12559-018-9611-8 Automatic Scientific Document Clustering Using Self-organized Multi-objective Differential Evolution Naveen Saini 1 · Sriparna Saha 1 · Pushpak Bhattacharyya 1 Received: 6 April 2018 / Accepted: 12 November 2018 / Published online: 19 December 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Document clustering is the partitioning of a given collection of documents into various K - groups based on some similarity/dissimilarity criterion. This task has applications in scope detection of journals/conferences, development of some automated peer-review support systems, topic-modeling, latest cognitive-inspired works on text summarization, and classification of documents based on semantics, etc. In the current paper, a cognitive-inspired multi-objective automatic document clustering technique is proposed which is a fusion of self-organizing map (SOM) and multi-objective differential evolution approach. The variable number of cluster centers are encoded in different solutions of the population to determine the number of clusters from a data set in an automated way. These solutions undergo various genetic operations during evolution. The concept of SOM is utilized in designing new genetic operators for the proposed clustering technique. In order to measure the goodness of a clustering solution, two cluster validity indices, Pakhira-Bandyopadhyay-Maulik index, and Silhouette index, are optimized simultaneously. The effectiveness of the proposed approach, namely self- organizing map based multi-objective document clustering technique (SMODoc clust) is shown in automatic classification of some scientific articles and web-documents. Different representation schemas including tf, tf-idf and word-embedding are employed to convert articles in vector-forms. Comparative results with respect to internal cluster validity indices, namely, Dunn index and Davies-Bouldin index, are shown against several state-of-the-art clustering techniques including three multi- objective clustering techniques namely MOCK, VAMOSA, NSGA-II-Clust, single objective genetic algorithm (SOGA) based clustering technique, K-means, and single-linkage clustering. Results obtained clearly show that our approach is better than existing approaches. The validation of the obtained results is also shown using statistical significant t tests. Keywords Clustering · Cluster validity indices · Self Organizing Map (SOM) · Differential Evolution (DE) · Polynomial mutation · Multi-objective Optimization (MOO) Introduction Background Document clustering [1] refers to partitioning of a given collection of documents into various K-groups based  Naveen Saini naveen.pcs16@iitp.ac.in; naveen.pcs16@gmail.com Sriparna Saha sriparna@iitp.ac.in Pushpak Bhattacharyya pb@iitp.ac.in 1 Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, 801103 Bihar, India on some similarity/dissimilarity criterion so that each document in a group is similar to other documents in the same group. Various applications of document clustering include: extraction of relevant topics [12], organization of documents as in digital libraries [63], creation of document taxonomy [22] such as in Yahoo, document summarization [25] etc. For the purpose of clustering, the value of K may or may not be known a priori. To determine the value of K in the collection of documents, traditional clustering approaches [44] like K-means [31], bisecting K-means [59], hierarchical clustering techniques [31] are required to be executed multiple times with various values of K. The qualities of different partitionings are measured with respect to some cluster validity indices, measuring the goodness of a partitioning by monitoring different intrinsic properties of clusters. Finally, the partitioning which corresponds to the optimal value of any cluster validity index is selected