NDLI: Hybrid data labeling algorithm for clustering large mixed type data

Content Provider	Springer Nature Link
Author	Sangam, Ravi Sankar Om, Hari
Copyright Year	2014
Abstract	Due to enormous growth in both volume and variety of data, clustering a very large database is a time-consuming process. To speed up clustering process, sampling has been recognized as a very utilitarian approach to reduce the dataset size in which a collection of data points are taken as a sample and then a clustering algorithm is applied to partitioning the data points in that sample into clusters. In this approach, the data points, that are not sampled, do not get their cluster labels. The process of allocating unlabeled data points into proper clusters has been well explored purely in numerical or categorical domain only, but not the both. In this paper, we propose a hybrid similarity coefficient to find the resemblance between an unlabeled data point and a cluster, based on the importance of categorical attribute values and the mean values of numerical attributes. Furthermore, we propose a Hybrid Data Labeling Algorithm (HDLA), based on this similarity coefficient to designate an appropriate cluster label to each unlabeled data point. We analyze its time complexity and perform various experiments using synthetic and real world datasets to demonstrate the efficacy of HDLA.
Starting Page	273
Ending Page	293
Page Count	21
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	45
Issue Number	2
e-ISSN	15737675
Language	English
Publisher	Springer US
Publisher Date	2014-12-14
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Clustering Data mining Data labeling Mixed type data Information Storage and Retrieval Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) IT in Business Document Preparation and Text Processing
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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