The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Enhancing Big Data Analytics and Recommendation Systems With Python: Hierarchical Clustering and Filtering Using Slicing Technique
Abstract
The advancement of technology has led to an exponential increase in the volume, velocity, and variety of data generated, necessitating the development of effective methods for analyzing and extracting valuable insights from large datasets. This research focuses on enhancing big data analytics and recommendation systems using Python, specifically employing hierarchical clustering and a filtering approach with the slicing technique. This study proposes a novel approach to leverage Python's capabilities in processing and analyzing big data. Hierarchical clustering algorithms organize and structure data into hierarchical groups, enabling efficient exploration and extraction of relevant information. Additionally, a filtering mechanism is integrated with the slicing technique, allowing for identifying and extracting specific subsets of data based on predefined criteria. Experiments are conducted using real-world datasets in the context of recommendation systems to evaluate the approach's effectiveness.
Related Content
|
Rashmi Gupta, Jeetendra Kumar, Suvarna Sharma.
© 2026.
32 pages.
|
|
Yashodeep Bharat Deshmukh, Abhishek Mukhopadhyay.
© 2026.
42 pages.
|
|
Suriya Murugan, Anandakumar Haldorai.
© 2026.
20 pages.
|
|
Meetu Malhotra, Rahul Awasthy.
© 2026.
34 pages.
|
|
Ismail Lamaakal, Bentaleb Youssef, Yassine Maleh, Ibrahim Ouahbi, Khalid El Makkaoui.
© 2026.
34 pages.
|
|
Muthmainnah Muthmainnah, Besse Darmawati, Abd. Rasyid, Sutejo Sutejo, Sri Haryatmo, Nurweni Saptawuryandari, Ahmad Al Yakin, Ismail Lamaakal.
© 2026.
28 pages.
|
|
Wasswa Shafik.
© 2026.
32 pages.
|
|
|