A new algorithm for fast mining frequent itemsets using N-lists
Mining frequent itemsets has emerged as a fundamental problem in data mining and plays
an essential role in many important data mining tasks. In this paper, we propose a novel …
an essential role in many important data mining tasks. In this paper, we propose a novel …
A hybrid approach to speed-up the k-means clustering method
TH Sarma, P Viswanath, BE Reddy - International Journal of Machine …, 2013 - Springer
Abstract k-means clustering method is an iterative partition-based method which for finite
data-sets converges to a solution in a finite time. The running time of this method grows …
data-sets converges to a solution in a finite time. The running time of this method grows …
An improvement to k-nearest neighbor classifier
P Viswanath, TH Sarma - 2011 IEEE Recent Advances in …, 2011 - ieeexplore.ieee.org
Non-parametric methods like Nearest neighbor classifier (NNC) and its variants such as k-
nearest neighbor classifier (k-NNC) are simple to use and often shows good performance in …
nearest neighbor classifier (k-NNC) are simple to use and often shows good performance in …
Fusion of multiple approximate nearest neighbor classifiers for fast and efficient classification
The nearest neighbor classifier (NNC) is a popular non-parametric classifier. It is a simple
classifier with no design phase and shows good performance. Important factors affecting the …
classifier with no design phase and shows good performance. Important factors affecting the …
Speeding-up the kernel k-means clustering method: A prototype based hybrid approach
TH Sarma, P Viswanath, BE Reddy - Pattern Recognition Letters, 2013 - Elsevier
Kernel k-means clustering method has been proved to be effective in identifying non-
isotropic and linearly inseparable clusters in the input space. However, this method is not a …
isotropic and linearly inseparable clusters in the input space. However, this method is not a …
Partition based pattern synthesis technique with efficient algorithms for nearest neighbor classification
Nearest neighbor (NN) classifier is a popular non-parametric classifier. It is conceptually a
simple classifier and shows good performance. Due to the curse of dimensionality effect, the …
simple classifier and shows good performance. Due to the curse of dimensionality effect, the …
Overlap pattern synthesis with an efficient nearest neighbor classifier
Nearest neighbor (NN) classifier is the most popular non-parametric classifier. It is a simple
classifier with no design phase and shows good performance. Important factors affecting the …
classifier with no design phase and shows good performance. Important factors affecting the …
Axioms to characterize efficient incremental clustering
S Bandyopadhyay, MN Murty - 2016 23rd International …, 2016 - ieeexplore.ieee.org
Although clustering is one of the central tasks in machine learning for the last few decades,
analysis of clustering irrespective of any particular algorithm was not undertaken for a long …
analysis of clustering irrespective of any particular algorithm was not undertaken for a long …
Data mining-based model for motion target trajectory prediction
L Liu, F Liu, B Ky - Journal of Intelligent & Fuzzy Systems, 2019 - content.iospress.com
Due to the increased development and applications of satellite communication, GPS
equipment, video tracking and other communication technologies, the trajectory prediction of …
equipment, video tracking and other communication technologies, the trajectory prediction of …
Speeding-up the prototype based kernel k-means clustering method for large data sets
TH Sarma, P Viswanath, A Negi - 2016 International Joint …, 2016 - ieeexplore.ieee.org
Kernel k-means is seen as a non-linear extension of the k-means clustering method, with
good performance in identifying non-isotropic and linearly inseparable clusters. However …
good performance in identifying non-isotropic and linearly inseparable clusters. However …