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@article{207542,
        author = {Aditya Kumar and Satya Pal and Abhishek Saxena and Maduresh kumar Yadav and Mohammad Jeelani},
        title = {Big Data and Data Mining : Review of Concepts and Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {1413-1418},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207542},
        abstract = {The volume, velocity, and variety of data generated across digital platforms — social media, financial technology, scientific instrumentation, and networked sensors — have grown at an unprecedented pace, making Big Data and Data Mining two of the most actively studied areas in computer science today. This paper presents an updated review of the core concepts, data attribute types, and algorithmic techniques (decision trees, genetic algorithms, and artificial neural networks) that underpin modern data mining practice. Building on classical foundations, the review also situates these concepts within recent developments in machine learning and deep learning, and surveys current application domains including healthcare analytics, financial technology, cybersecurity/IoT intrusion detection, and cloud-based big data mining. In addition to summarizing techniques, this paper highlights the persistent challenges of scalability, data heterogeneity, and the gap between data-rich repositories and actionable knowledge (“information poverty”). Visual illustrations are used throughout to clarify the three V's of Big Data, the knowledge-discovery pipeline, and representative algorithmic architectures. The review concludes that while data mining methodology has matured considerably, opportunities for improvement remain wide open, particularly as data continues to grow in scale and complexity.},
        keywords = {data; big data; data mining; machine learning; deep learning; knowledge discovery The main purpose of data mining is classification or forecast. In classification, there are a lot of cases that can be simulated such as in a product ad can be sorted out where the respondents who are interested in the ad and which are not, from here can be developed about what is behind someone interested in the ad. this information is expected to be predicted through data mining.},
        month = {August},
        }