Pub Date : 2021-11-10DOI: 10.54105/ijdm.b1615.111221
D. Sharma, Dr. Priti Sharma
Data mining is a rapidly developing technology that has enriched a lot of field such as business analysis, market analysis, weather forecasting, stock market analysis and many more. It starts with collecting data sets from reliable sources and pre-processing that data. There are some anomalies associated with data collected in large volumes such as outliers, missing values, and duplicated values. Remove these kinds of anomalies is teamed as pre-processing of data. In this paper, collection of weather data and pre-processing it for rainfall prediction model using Rapid Miner tool has been discussed. Also, artificial neural network data mining techniques is used to design a rainfall prediction model. ANN classification techniques is a complex data mining technique results in high accuracy in prediction of rainfall.
{"title":"Design and Implementation of Rainfall Prediction Model using Supervised Machine Learning Data Mining Techniques","authors":"D. Sharma, Dr. Priti Sharma","doi":"10.54105/ijdm.b1615.111221","DOIUrl":"https://doi.org/10.54105/ijdm.b1615.111221","url":null,"abstract":"Data mining is a rapidly developing technology that has enriched a lot of field such as business analysis, market analysis, weather forecasting, stock market analysis and many more. It starts with collecting data sets from reliable sources and pre-processing that data. There are some anomalies associated with data collected in large volumes such as outliers, missing values, and duplicated values. Remove these kinds of anomalies is teamed as pre-processing of data. In this paper, collection of weather data and pre-processing it for rainfall prediction model using Rapid Miner tool has been discussed. Also, artificial neural network data mining techniques is used to design a rainfall prediction model. ANN classification techniques is a complex data mining technique results in high accuracy in prediction of rainfall.","PeriodicalId":375116,"journal":{"name":"Indian Journal of Data Mining","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128644837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human-Computer Interaction (HCI) is no longer the sole study of information technology or computer science but now it has covered the area of medical, entertainment, etc. As the application of HCI is increasing, so is the requirement of students to work in a multidisciplinary environment. Making students comfortable working in a multidisciplinary environment is not an easy task. The students are required to make pretty much aware and comfortable with the underlying problem statement. The evolution of HCI in education is to make sure that students can understand the concepts and working of the model in a more effective way. The goal of this project is to create a web-based e-Learning tool, ‘Path Finding Visualizer’. It refers to computing an optimal route between the specified start node and goal nodes visualizing shortest path algorithms. The conceptual application of the project is illustrated by the implementation of algorithms like Dijkstra’s, A*, and DFS. The end product is a web application so that any user can easily see and learn the working of the algorithms through perceivable visualizations. The user-friendliness of the project provides the user with easy instructions on how to operate it. The initial results of using the application show promised benefits of the e-Learning tool towards students getting a good understanding of shortest paths algorithms.
{"title":"Human Computer Interaction in Education","authors":"Ananya Khurana, Rohan Raj, Satender Kumar, Ms. Neha Garg","doi":"10.54105/ijdm.a1625.053123","DOIUrl":"https://doi.org/10.54105/ijdm.a1625.053123","url":null,"abstract":"Human-Computer Interaction (HCI) is no longer the sole study of information technology or computer science but now it has covered the area of medical, entertainment, etc. As the application of HCI is increasing, so is the requirement of students to work in a multidisciplinary environment. Making students comfortable working in a multidisciplinary environment is not an easy task. The students are required to make pretty much aware and comfortable with the underlying problem statement. The evolution of HCI in education is to make sure that students can understand the concepts and working of the model in a more effective way. The goal of this project is to create a web-based e-Learning tool, ‘Path Finding Visualizer’. It refers to computing an optimal route between the specified start node and goal nodes visualizing shortest path algorithms. The conceptual application of the project is illustrated by the implementation of algorithms like Dijkstra’s, A*, and DFS. The end product is a web application so that any user can easily see and learn the working of the algorithms through perceivable visualizations. The user-friendliness of the project provides the user with easy instructions on how to operate it. The initial results of using the application show promised benefits of the e-Learning tool towards students getting a good understanding of shortest paths algorithms.","PeriodicalId":375116,"journal":{"name":"Indian Journal of Data Mining","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123507782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.54105/ijdm.a1624.053123
Vinayak Jain
Strong adoption of Internet and Communication technologies across industries in the last two decades has led to large-scale digitization of business processes. While this has helped in the instant availability of information, over the period, the source and amount of this information have increased multi-fold giving rise to Big Data. With the increase in volume, the relevance of data in its raw format continues to decrease over time. According to HACE Theorem, Big Data has autonomous sources being distributed and decentralized data in a complex relationship with each other. Making sense of this ever-growing large pool of data has become increasingly difficult and has created a new problem waning the initial gains made via the digitization of systems and processes. This gave rise to the evolution of multiple Data Mining techniques that have helped to classify large volumes of data into relevant segments and drive value to help provide meaningful information. To extract and discover knowledge from data, Knowledge Discovering Databases (KDD) help in the refining of data. This paper discusses various data mining techniques that help to identify patterns and relationships to help make business decisions using data analysis. Furthermore, the Data Fusion method is reviewed which deals with joint analysis of multiple inter-related datasets providing multiple complementary views to help further with precise decision-making.
{"title":"An Overview on Data Mining and Data Fusion","authors":"Vinayak Jain","doi":"10.54105/ijdm.a1624.053123","DOIUrl":"https://doi.org/10.54105/ijdm.a1624.053123","url":null,"abstract":"Strong adoption of Internet and Communication technologies across industries in the last two decades has led to large-scale digitization of business processes. While this has helped in the instant availability of information, over the period, the source and amount of this information have increased multi-fold giving rise to Big Data. With the increase in volume, the relevance of data in its raw format continues to decrease over time. According to HACE Theorem, Big Data has autonomous sources being distributed and decentralized data in a complex relationship with each other. Making sense of this ever-growing large pool of data has become increasingly difficult and has created a new problem waning the initial gains made via the digitization of systems and processes. This gave rise to the evolution of multiple Data Mining techniques that have helped to classify large volumes of data into relevant segments and drive value to help provide meaningful information. To extract and discover knowledge from data, Knowledge Discovering Databases (KDD) help in the refining of data. This paper discusses various data mining techniques that help to identify patterns and relationships to help make business decisions using data analysis. Furthermore, the Data Fusion method is reviewed which deals with joint analysis of multiple inter-related datasets providing multiple complementary views to help further with precise decision-making.","PeriodicalId":375116,"journal":{"name":"Indian Journal of Data Mining","volume":"22 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125688975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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