P
Pradeep Kumar
Researcher at Indian Institute of Management Lucknow
Publications - 48
Citations - 814
Pradeep Kumar is an academic researcher from Indian Institute of Management Lucknow. The author has contributed to research in topics: Cluster analysis & Recommender system. The author has an hindex of 13, co-authored 48 publications receiving 565 citations. Previous affiliations of Pradeep Kumar include Institute for Development and Research in Banking Technology & Indian Institute of Management Ahmedabad.
Papers
More filters
Journal ArticleDOI
HeteClass: A Meta-path based framework for transductive classification of objects in heterogeneous information networks
TL;DR: A novel meta-path based framework, HeteClass, for transductive classification of target type objects, which is flexible to utilize any suitable classification algorithm for transductionive classification and can be applied on heterogeneous information networks with arbitrary network schema.
Journal ArticleDOI
Interplay between trust, information privacy concerns and behavioural intention of users on online social networks
TL;DR: It is revealed that intention to disclose information mediates the relationship between trust in the website and the intention to interact with others, and the trust in website also plays a crucial role while determining the information privacy concerns in the OSN.
Journal ArticleDOI
Auxiliary Flexibility in Healthcare Delivery System: An Integrative Framework and Implications
TL;DR: In this article, the authors conceptualized auxiliary flexibility as a new dimension of flexibility in the healthcare delivery system which provides internal strength to the healthcare organizations to reduce their service variability and identify its sources to provide varied services to the patients.
Journal ArticleDOI
An upper approximation based community detection algorithm for complex networks
TL;DR: A novel rough set based community detection algorithm capable of uncovering true community structure in networks, be it disjoint, overlapping or nested is proposed and significantly outperforms state-of-the-art techniques.
Journal ArticleDOI
A New Similarity Metric for Sequential Data
TL;DR: A similarity preserving function called Sequence and Set Similarity Measure S3M that captures both the order of occurrence of items in sequences and the constituent items of sequences is proposed.