Volume-3 Issue-2, November 2023 – Indian Journal of Data Mining (IJDM)

B1629113223

IJDM Journal — Sat, 25 Nov 2023 09:42:17 +0000

The Indian Journal of Data Mining (IJDM) has ISSN 2582-9246 (online), an open-access, peer-reviewed, periodical half-yearly international journal, which is published by Lattice Science Publication (LSP) in May and November. The journal aims to publish high-quality peer–reviewed original articles in the area of Data Mining that covers Data Mining, Data Science, Big Data, Data Warehouse, Visualization, Security, Privacy, Big DaaS, Scalable Computing, Cloud Computing, Knowledge Discovery, Integration, Transformation, Information Retrieval, Social Data and Semantics, Mining Functions, Data Regression, Data Classification, Anomaly Detection, Data Clustering, Data Association, Data Cleaning, Feature Selection and Extraction, Data Mining Algorithms, Apriori Decision Tree, Generalized Linear Models, k-Means, Minimum Description Length, Naive Bayes Non-Negative Matrix Factorization, 0-Cluster, Support Vector Machines, Data Preparation, Mining Unstructured Data, Artificial Intelligence, Future Directions and Challenges in Data Mining and Industrial Challenges in Data Mining. #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #k-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #PhD ademic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Pre-Processing and Normalization of the Historical Weather Data Collected from Secondary Data Source for Rainfall Prediction
Deepak Sharma¹, Priti Sharma²

¹Deepak Sharma, Research Scholar, Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak (Haryana), India.

²Dr. Priti Sharma, Assistant professor, Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak (Haryana), India.

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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the twenty first century, data analysis has become the talk of the town. Almost every company or organization depends on data analysis for taking future decision. The most important step in data analysis after data collection is the preprocessing of the collected data. The main aim of data analysis is to find meaningful pattern by processing large amount of data. In data preprocessing, the inconsistency of collected data has been removed. After storing data for a relatively longer period, it becomes noisy and inconsistent. While measuring various parameter due to error in the instrument or human error, the value become incorrect or invalid. It is necessary to remove the invalid data otherwise it will deflect the results and produce error in the prediction. In this work preprocessing of the weather data has been analyzed for rainfall prediction using data mining.

Keywords: Data Mining, Data Collection, Data Preprocessing, Secondary Data Sources, Weather Data, Rainfall Prediction, Machine Learning.
Article of the Scope: Data Mining

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B1627113223

IJDM Journal — Sat, 25 Nov 2023 09:39:03 +0000

Comparison of the Proposed Rainfall Prediction Model Designed using Data Mining Techniques with the Existing Rainfall Prediction Methods
Deepak Sharma¹, Priti Sharma²

¹Deepak Sharma, DCSA, Maharshi Dayanand University, Rohtak (Haryana), India.

²Dr. Priti Sharma, DCSA, Maharshi Dayanand University, Rohtak (Haryana), India.

Abstract: Weather prediction is a very old practice and people are doing predictions about weather much before the discovery of the weather measuring instrument. In ancient times, people give weather predictions by observing the sky for a long time and patterns of the stars at night. Things are a bit different now. People more relay on the past trends and patterns followed by the weather parameters. Data mining and machine leaning is used to analysis the historical weather trends by analyzing weather data using various Data mining techniques. In this paper three rainfall prediction model based on data mining techniques are proposed and compared with the other rainfall prediction model. The comparison has been done on the basis of accuracy, precision, Recall and RMSE. The proposed models are based on ensemble methods such as bagging, boosting, and stacking. Ensemble methods are used to enhance the overall performance and accuracy of the prediction. In both bagging and boosting based proposed rainfall prediction models, artificial neural network is used as a base leaner and daily weather data from the year 1988 to 2022 is used. In stacking based proposed rainfall prediction model, random forest, Logistic regression, and K-Nearest neighbor are used as base leaners or level -0 learners and Artificial neural network is used as Meta model.

Keywords: Data Mining, Data Collection, Secondary Data Sources, Weather Data, Rainfall Prediction, Machine Learning.
Article of the Scope: Data Mining

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B1626113223

IJDM Journal — Sat, 25 Nov 2023 09:34:46 +0000

Collection of Weather Data from Authentic Websites and Secondary Data Sources for Rainfall Prediction
Deepak Sharma¹, Priti Sharma²

¹Deepak Sharma, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak (Haryana), India.

²Dr. Priti Sharma, Assistant Professor, Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak (Haryana), India.

Abstract: The field of data mining and machine learning has been grown many folds from the last two decades. Almost every other problem can be solved using data mining and this becomes the most tempting part of it for the scientist and researchers all over the world. Data mining can be viewed as a process of discovering knowledge. This discovery of knowledge starts with the collection of data and ends with the acquired knowledge in the form of patterns. Data collection lays the foundation for the process of knowledge discovery. In this paper, various secondary data sources from where data can be collected for rainfall prediction are deeply studied and analyzed. Some of these authentic websites and secondary data sources are NCDC (National climate data center), Kaggle, Datahub.io, UCI machine learning repository, Earth Data etc. The data collected from these secondary data sources for rainfall prediction have been critically analyzed and compared on the parameters of Accuracy, Completeness, reliability, relevance, and timeliness.

Keywords: Data Mining, Data Collection, Secondary Data Sources, Weather Data, Rainfall Prediction, Machine Learning.
Article of the Scope: Data Mining

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