AI-Driven Data Mining Algorithms in Change Management: Leveraging Classification and Clustering for Data-Driven Project Optimization
Keywords:
Data Mining, ClassificationAbstract
Data mining algorithms are pivotal in extracting valuable insights from large datasets, especially within AI-driven Change Management and Project Management frameworks. This paper provides a comprehensive review of data mining algorithms for classification and clustering tasks, focusing on three main algorithms: decision trees, k-means, and DBSCAN. By exploring the application of these algorithms in managing organizational changes and optimizing project workflows, we demonstrate their effectiveness in predicting project outcomes, segmenting stakeholders, and enabling data-driven decision-making. Additionally, we examine the recent advancements and challenges in the field of AI-enhanced data mining for classification and clustering, particularly in the context of improving project management efficiency and supporting adaptive change management strategies.
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References
Vemoori, Vamsi. "Envisioning a Seamless Multi-Modal Transportation Network: A Framework for Connected Intelligence, Real-Time Data Exchange, and Adaptive Cybersecurity in Autonomous Vehicle Ecosystems." Australian Journal of Machine Learning Research & Applications 4.1 (2024): 98-131.
Sadhu, Ashok Kumar Reddy, et al. "Enhancing Customer Service Automation and User Satisfaction: An Exploration of AI-powered Chatbot Implementation within Customer Relationship Management Systems." Journal of Computational Intelligence and Robotics 4.1 (2024): 103-123.
Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
Perumalsamy, Jegatheeswari, Chandrashekar Althati, and Lavanya Shanmugam. "Advanced AI and Machine Learning Techniques for Predictive Analytics in Annuity Products: Enhancing Risk Assessment and Pricing Accuracy." Journal of Artificial Intelligence Research 2.2 (2022): 51-82.
Venkatasubbu, Selvakumar, Jegatheeswari Perumalsamy, and Subhan Baba Mohammed. "Machine Learning Models for Life Insurance Risk Assessment: Techniques, Applications, and Case Studies." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 423-449.
Mohammed, Subhan Baba, Bhavani Krothapalli, and Chandrashekar Althat. "Advanced Techniques for Storage Optimization in Resource-Constrained Systems Using AI and Machine Learning." Journal of Science & Technology 4.1 (2023): 89-125.
Krothapalli, Bhavani, Lavanya Shanmugam, and Subhan Baba Mohammed. "Machine Learning Algorithms for Efficient Storage Management in Resource-Limited Systems: Techniques and Applications." Journal of Artificial Intelligence Research and Applications 3.1 (2023): 406-442.
Devan, Munivel, Chandrashekar Althati, and Jegatheeswari Perumalsamy. "Real-Time Data Analytics for Fraud Detection in Investment Banking Using AI and Machine Learning: Techniques and Case Studies." Cybersecurity and Network Defense Research 3.1 (2023): 25-56.
Althati, Chandrashekar, Jegatheeswari Perumalsamy, and Bhargav Kumar Konidena. "Enhancing Life Insurance Risk Models with AI: Predictive Analytics, Data Integration, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 448-486.
Selvaraj, Amsa, Bhavani Krothapalli, and Lavanya Shanmugam. "AI and Machine Learning Techniques for Automated Test Data Generation in FinTech: Enhancing Accuracy and Efficiency." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 329-363.
Makka, A. K. A. “Implementing SAP on Cloud: Leveraging Security and Privacy Technologies for Seamless Data Integration and Protection”. Internet of Things and Edge Computing Journal, vol. 3, no. 1, June 2023, pp. 62-100, https://thesciencebrigade.com/iotecj/article/view/286.
Pelluru, Karthik. "Unveiling the Power of IT DataOps: Transforming Businesses across Industries." Innovative Computer Sciences Journal 8.1 (2022): 1-10.
Konidena, Bhargav Kumar, Jesu Narkarunai Arasu Malaiyappan, and Anish Tadimarri. "Ethical Considerations in the Development and Deployment of AI Systems." European Journal of Technology 8.2 (2024): 41-53.
Devan, Munivel, et al. "AI-driven Solutions for Cloud Compliance Challenges." AIJMR-Advanced International Journal of Multidisciplinary Research 2.2 (2024).
Katari, Monish, Gowrisankar Krishnamoorthy, and Jawaharbabu Jeyaraman. "Novel Materials and Processes for Miniaturization in Semiconductor Packaging." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 2.1 (2024): 251-271.
Tatineni, Sumanth, and Naga Vikas Chakilam. "Integrating Artificial Intelligence with DevOps for Intelligent Infrastructure Management: Optimizing Resource Allocation and Performance in Cloud-Native Applications." Journal of Bioinformatics and Artificial Intelligence 4.1 (2024): 109-142.
Sistla, Sai Mani Krishna, and Bhargav Kumar Konidena. "IoT-Edge Healthcare Solutions Empowered by Machine Learning." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 126-135.
Katari, Monish, Lavanya Shanmugam, and Jesu Narkarunai Arasu Malaiyappan. "Integration of AI and Machine Learning in Semiconductor Manufacturing for Defect Detection and Yield Improvement." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 3.1 (2024): 418-431.
Tembhekar, Prachi, Munivel Devan, and Jawaharbabu Jeyaraman. "Role of GenAI in Automated Code Generation within DevOps Practices: Explore how Generative AI." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 500-512.
Peddisetty, Namratha, and Amith Kumar Reddy. "Leveraging Artificial Intelligence for Predictive Change Management in Information Systems Projects." Distributed Learning and Broad Applications in Scientific Research 10 (2024): 88-94.
Venkataramanan, Srinivasan, et al. "Leveraging Artificial Intelligence for Enhanced Sales Forecasting Accuracy: A Review of AI-Driven Techniques and Practical Applications in Customer Relationship Management Systems." Australian Journal of Machine Learning Research & Applications 4.1 (2024): 267-287.
