S
Sandhya Harikumar
Researcher at Amrita Vishwa Vidyapeetham
Publications - 28
Citations - 166
Sandhya Harikumar is an academic researcher from Amrita Vishwa Vidyapeetham. The author has contributed to research in topics: Clustering high-dimensional data & Canopy clustering algorithm. The author has an hindex of 6, co-authored 22 publications receiving 108 citations.
Papers
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Journal ArticleDOI
K-Medoid Clustering for Heterogeneous DataSets☆
Sandhya Harikumar,Surya Pv +1 more
TL;DR: A new similarity measure in the form of triplet is proposed to find the distance between two data objects with heterogeneous attribute types to outperforms traditional clustering algorithms for mixed datasets.
Proceedings ArticleDOI
Apriori algorithm for association rule mining in high dimensional data
TL;DR: A variant of Apriori algorithm is proposed using the concept of QR decomposition for reducing the dimensions thereby reducing the complexity of the traditional A Priori algorithm.
Proceedings ArticleDOI
Logistic regression within DBMS
Jackson Isaac,Sandhya Harikumar +1 more
TL;DR: This paper proposes an alternative strategy, based on SQL and UDFs, to integrate the logistic regression for data categorization as well as prediction query processing within DBMS and shows the viability and validity of this approach for predicting the class of a given query.
Proceedings ArticleDOI
Hybridized fragmentation of very large databases using clustering
TL;DR: This paper presents a unique approach towards hybridized fragmentation, by applying subspace clustering algorithm, to come up with a set of fragments which partitions the data with respect to tuples as well as attributes, giving good hybridized fragments for distributed databases.
Journal ArticleDOI
Comparative analysis of multiple machine learning algorithms for epileptic seizure prediction
TL;DR: This research concentrates on the performance analysis of various machine learning algorithms on recorded EEG data to find the best model, which can be used to create an ensemble model for better learning.