Keywords : data Mining

Analyzing and Predicting Covid-19 Dataset in India using Data Mining with Regression Analysis

Rajesh P.; Vetrivel Govindarasu

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue 7S, Pages 91-95
DOI: 10.47392/irjash.2021.216

COVID-19 is a disease caused by coronavirus. 'CO' stands for corona, 'VI' for virus, and 'D' for disease. Formerly, this disease was referred to as '2019 novel coronavirus. The data mining is the best tools for analyzing and predicting the hidden information with the help of pre-existing dataset. The covid analysis and prediction for consider different related parameters namely name of the states, total cases, today cases, active cases, discharged cases, today discharged cases, overall death and today deaths. In this paper, taking consideration into analyzing and predicting covid dataset using statistical techniques namely regression model. Numerical illustrations also provide to prove the results and discussions.

Analyzing Of Clustering Algorithms for Achieving High Evaluation Metrics

Sarumathi S.; Navinkumar K.; Vadivel Kumar T.; Sharan Viswanathan R.

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICITCA-2021 5S, Pages 30-37
DOI: 10.47392/irjash.2021.136

In the real data world, there are various clustering algorithms available in data mining. The data available from the different data sources may be huge in instances, attributes and in different formats. The clustering algorithms available are assessed based on how the algorithm cluster the given data and find its parametric values.  The clustering of data may end in inappropriate results if the algorithm is not chosen wisely. This paper proposes a comparison between diverse clustering algorithms such as K Means clustering, Mini-Batch K Means clustering, Hierarchical clustering, Bagging and Boosting by figuring out clustering strategies using high dimensional datasets on each algorithm above. After the process of data cleaning in dataset, we have clustered the datasets and compared the summary of each to showcase the comparability of difference in their strategical values such as Clustering tendency, clustering quality and data driven approach for evaluating the number of clusters, Normalized Mutual Information (NMI) metric and provide an idea to choose the algorithm for clustering the data effectively. And as a result, Local Clustering Coefficient (LCC) with K-means clustering bunching method performs better than the other clustering algorithms and the results are reported.

Extracting top competitors from unorganized data-Review

Kavinya P; Nanthini K; Indumathi B

International Research Journal on Advanced Science Hub, 2019, Volume 1, Issue 1, Pages 10-16
DOI: 10.47392/irjash.2019.02

The ability to make a product more desirable to consumers than competition is central to the success of every competitive business. The web application allows the user to see products and their functionalities together with the potential to comment on the product and can also show other customers ' comments. A potential customer finds it difficult to read and determine from the broad comments. The competition of two items based upon market segments that both can cover is determined by this approach. A "CMiner" algorithm is provided to find the top competitors for a particular item to predict competition using the customer reviews. This system returns the competitors of products correctly and reliably, as compared to previous models based on subjective and comparative Web expressions. Business organizations are not only able to identify competitiveness, but they are also able to benefit from meeting user needs.