Clustering of Data with Mixed Attributes Based on Unified Similarity Metric

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Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Big Data
Format: PDF
Most of the clustering approaches are applicable to purely numerical data or purely categorical data but not both. There exists an awkward gap between the similarity metrics for categorical and numerical data, so it is a non trivial task for clustering of data with mixed attributes. A general clustering algorithm for based on object cluster similarity is framed which clusters the data with mixed attributes. Moreover, clustering techniques are applied in Educational Data Mining (EDM) to group of students according to their customized features.
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