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
Rough clustering of sequential data
TL;DR: The rough clusters resulting from the proposed algorithm provide interpretations of different navigation orientations of users present in the sessions without having to fit each object into only one group.
Journal ArticleDOI
A web recommendation system considering sequential information
TL;DR: This work has developed a novel system that considers sequential information present in web navigation patterns, along with content information, which helps in capturing the multiple interests of users in recommendation systems.
Journal ArticleDOI
DNNRec: A novel deep learning based hybrid recommender system
TL;DR: The solution alleviates the cold start problem by integrating side information about users and items into a very deep neural network and uses a decreasing learning rate in conjunction with increasing weight decay, the values cyclically varied across epochs to further improve accuracy.
Journal ArticleDOI
Fuzzy based clustering algorithm for privacy preserving data mining
TL;DR: This paper addresses the problem of PPDM by transforming the attributes to fuzzy attributes, and the individual privacy is also maintained, as one cannot predict the exact value, at the same time, better accuracy of mining results is achieved.
Book
Pattern Discovery Using Sequence Data Mining: Applications and Studies
TL;DR: Pattern Discovery Using Sequence Data Mining: Applications and Studies provides a comprehensive view of sequence mining techniques and presents current research and case studies in pattern discovery in sequential data by researchers and practitioners.