Clustering Relational Database Entities Using K-means

Abstract

Abstract-The fast evolution of hardware and the internet made large volumes of data more accessible. This data is composed of heterogeneous data types such as text, numbers, multimedia, and others. Non-overlapping research communities work on processing homogeneous data types. Nevertheless, from the user perspective, these heterogeneous data types should behave and be accessed in a similar fashion. Processing heterogeneous data types, which is Heterogeneous Data Mining (HDM), is a complex task. However, the HDM by Unified Vectorization (HDM-UV) seems to be an appropriate solution for this problem because it permits to process the heterogeneous data types simultaneously. In this paper, we use K-means and SelfOrganizing Maps for simultaneously processing textual and numerical data types by UV. We evaluate how the HDM-UV improves the clustering results of these two algorithms (SOM, K-means) by comparing them to the traditional homogeneous data processing. Furthermore, we compare the clustering results of the two algorithms applied to a data integration problem.

Research and Optimization of Data Classification using K-means Clustering and Affinity Propagation Technique

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“…The data point is the representation of every input sentence. The data point is associated with certain mathematical values which are calculated using vectorization techniques namely term frequency and inverse document frequency [3]. The vector is computed by performing the dot product on scalar quantities such as the parameter value considered and the mathematical value of the text used during the procedure of classification.…”