14 research outputs found
Binary Particle Swarm Optimization based Biclustering of Web usage Data
Web mining is the nontrivial process to discover valid, novel, potentially
useful knowledge from web data using the data mining techniques or methods. It
may give information that is useful for improving the services offered by web
portals and information access and retrieval tools. With the rapid development
of biclustering, more researchers have applied the biclustering technique to
different fields in recent years. When biclustering approach is applied to the
web usage data it automatically captures the hidden browsing patterns from it
in the form of biclusters. In this work, swarm intelligent technique is
combined with biclustering approach to propose an algorithm called Binary
Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The
main objective of this algorithm is to retrieve the global optimal bicluster
from the web usage data. These biclusters contain relationships between web
users and web pages which are useful for the E-Commerce applications like web
advertising and marketing. Experiments are conducted on real dataset to prove
the efficiency of the proposed algorithms
COVID-19 mortality rate prediction for India using statistical neural networks and gaussian process regression model
The primary purpose of this research is to identify the best COVID-19
mortality model for India using regression models and is to estimate
the future COVID-19 mortality rate for India. Specifically, Statistical
Neural Networks ( Radial Basis Function Neural Network (RBFNN),
Generalized Regression Neural Network (GRNN)), and Gaussian Process
Regression (GPR) are applied to develop the COVID-19 Mortality Rate
Prediction (MRP) model for India. For that purpose, there are two types
of dataset used in this study: One is COVID-19 Death cases, a Time
Series Data and the other is COVID-19 Confirmed Case and Death Cases
where Death case is dependent variable and the Confirmed case is an
independent variable. Hyperparameter optimization or tuning is used in
these regression models, which is the process of identifying a set of
optimal hyperparameters for any learning process with minimal error.
Here, sigma (\u3c3) is a hyperparameter whose value is used to
constrain the learning process of the above models with minimum Root
Mean Squared Error (RMSE). The performance of the models is evaluated
using the RMSE and 'R2 values, which shows that the GRP model performs
better than the GRNN and RBFNN
Recommendation of Web Pages using Weighted K- Means Clustering
Web Recommendation Systems are implemented by using collaborative filtering approach. It is a specific type of information filtering system that aims to predict the user browsing activity and then recommend to the user web pages items that are likely to be of interest. In this paper, a new recommendation system is proposed by using Weighted K-Means clustering approach to predict the user’s navigational behavior. The proposed recommendation system based on Weighted K-Means clustering performs well when compared to K-Means algorithm. The performance of the comparative analysis is presented through experimental results