Comparative Analysis of Clustering Techniques in Weakly Supervised Data
$30-250 USD
着払い
This research project focuses on exploring and comparing the effectiveness of constraint-based and semi-supervised clustering algorithms in the context of weakly supervised data. The project aims to identify which methodologies are more adept at handling datasets where labels are either incomplete, imprecise, or partially incorrect.
The study will be conducted using two distinct datasets from different domains – one from the financial sector (such as stock market data) and another from a contrasting field like healthcare or social media. This approach allows for a comprehensive analysis across varied data types, including numerical and textual data.
Key steps in the project include selecting appropriate datasets, preprocessing them to fit a weakly supervised learning model (by introducing label noise and incomplete labeling), and implementing various constraint-based and semi-supervised clustering algorithms. The algorithms’ performance will be evaluated using metrics such as Purity, Rand Index, and computational efficiency.
By comparing these algorithms in diverse weakly supervised scenarios, the project seeks to uncover insights into their adaptability and accuracy, providing valuable contributions to the field of machine learning, especially in scenarios where perfect labeling is unfeasible. This research holds significant implications for practical applications, offering guidance on choosing suitable clustering techniques in real-world data environments characterized by label deficiencies.
--->>> For more and detailed information about project please have a look at the attached file.
プロジェクトID: #37764980
プロジェクトについて
22人のフリーランサーが、平均$173 で、この仕事に入札しています。
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