Volume-3 Issue-1, May 2023 – Indian Journal of Data Mining (IJDM) https://www.ijdm.latticescipub.com Exploring Innovation | ISSN: 2582-9246 (Online) | A Periodical Journal | Reg. No: C/1383209 | Published by Lattice Science Publication (LSP) Fri, 29 Dec 2023 09:52:00 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 https://www.ijdm.latticescipub.com/wp-content/uploads/2021/03/cropped-IJDM-WFS-32x32.png Volume-3 Issue-1, May 2023 – Indian Journal of Data Mining (IJDM) https://www.ijdm.latticescipub.com 32 32 B1630113223 https://www.ijdm.latticescipub.com/portfolio-item/b1630113223/ Wed, 20 Dec 2023 10:17:02 +0000 https://www.ijdm.latticescipub.com/?post_type=portfolio&p=1005 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

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]]> 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

Myers-Briggs Personality PredictionCROSSMARK Color horizontal
Rohith Muralidharan1, Neenu Kuriakose2, Sangeetha J3

1Rohith Muralidharan, Department of Computer Science, St. Albert’s College (Autonomous), Ernakulam, India.

2Neenu Kuriakose, Department of Computer Science, St. Albert’s College (Autonomous), Ernakulam, India.

3Sangeetha J, Department of Computer Science, St. Albert’s College (Autonomous), Ernakulam, India. 

Manuscript received on 28 April 2023 | Revised Manuscript received on 08May 2023 | Manuscript Accepted on 15 May 2023 | Manuscript published on 30 December 2023 | PP: 11-19 | Volume-3 Issue-1 May 2023 | Retrieval Number: 100.1/ijdm.B1630113223 | DOI: 10.54105/ijdm.B1630.053123

Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© 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: The Myers-Briggs Type Indicator (MBTI) is one of the most commonly used tool for assessing an individual’s personality. This tool allows us to identify the psychological proclivity in the way they take decisions and perceive the world. MBTI has it’s applications spread across several fields which include career development and personal growth. This test consists of a set of questions which are specifically designed to evaluate and measure an individual’s choices based on four dichotomies – Extraversion (E) vs. Introversion (I), Sensing (S) vs. Intuition (N), Thinking (T) vs. Feeling (F), and Judging (J) vs. Perceiving (P). Myers-Briggs Personality Prediction project aims to develop and deploy a system using machine learning which is capable of predicting one’s MBTI personality type based on their online written interactions such as social media posts, comments, blogs etc. This project has significant implications for various applications, including improving customer experience, optimizing team dynamics, and developing personalized coaching programs. Through this project, we hope to gain a deeper understanding of how language use and personality type are related and to develop a robust tool for personality prediction.

Keywords: Python, Machine Learning, XG Boost, Supervised learning.
Article of the Scope: Machine Learning

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]]> A1625053123 https://www.ijdm.latticescipub.com/portfolio-item/a1625053123/ Sat, 21 Jan 2023 11:04:45 +0000 https://www.ijdm.latticescipub.com/?post_type=portfolio&p=756 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

The post A1625053123 appeared first on Indian Journal of Data Mining (IJDM).

]]> 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

Human Computer Interaction in EducationCROSSMARK Color horizontal
Ananya Khurana1, Rohan Raj2, Satender Kumar3, Neha Garg4

1Ananya Khurana, Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad (Haryana), India.

2Rohan Raj, Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad (Haryana), India.

3Satender Kumar, Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad (Haryana), India.

4Ms. Neha Garg, Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad (Haryana), India.

Manuscript received on 04 January 2023 | Revised Manuscript received on 16 January 2023 | Manuscript Accepted on 15 May 2023 | Manuscript published on 30 May 2023 | PP: 6-9 | Volume-3 Issue-1 May 2023 | Retrieval Number: 100.1/ijdm.A1625053123 | DOI: 10.54105/ijdm.A1625.053123

Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© 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: 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.  

Keywords: Path Finding Visualizer, Graph, Education, Data Structures
Article of the Scope: Visualization

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]]> A1624053123 https://www.ijdm.latticescipub.com/portfolio-item/a1624053123/ Sat, 24 Dec 2022 07:39:24 +0000 https://www.ijdm.latticescipub.com/?post_type=portfolio&p=711 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

The post A1624053123 appeared first on Indian Journal of Data Mining (IJDM).

]]> 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

An Overview on Data Mining and Data FusionCROSSMARK Color horizontal
Vinayak Jain

Vinayak Jain, Department of Computer Science, SRM University, NCR-Delhi, Sonepat (Haryana), India. 

Manuscript received on 12 December 2022 | Revised Manuscript received on 22 December 2022 | Manuscript Accepted on 15 May 2023 | Manuscript published on 30 May 2023 | PP: 1-5 | Volume-3 Issue-1 May 2023 | Retrieval Number: 100.1/ijdm.A1624053123 | DOI:10.54105/ijdm.A1624.053123

Open Access | Editorial and Publishing Policies | Cite | Zenodo | Indexing and Abstracting
© 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: 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. 

Keywords: Big Data, Data Mining, Data Fusion, KDD, HACE Theorem
Article of the Scope: Data Mining

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