A Universal Similarity Model for Transactional Data Clustering

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Provided by: Auricle Technologies
Topic: Big Data
Format: PDF
Data mining methods are used to extract hidden knowledge from large database. Data partitioning methods are used to group up the relevant data values. Similar data values are grouped under the same cluster. K-means and Partitioning Around Medoids (PAM) clustering algorithms are used to cluster numerical data. Distance measures are used to estimate the transaction similarity. Data partitioning solutions are identified using the cluster ensemble models. The ensemble information matrix presents only cluster data point relations. Ensembles based clustering techniques produces final data partition based on incomplete information.
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