Kleinberg’s Hyper-Richness Based Fuzzy Partition Clustering for Efficient Bi-Temporal Data
L. Jaya Singh Dhas1, B. Mukunthan2
1L. Jaya Singh Dhas, Research Scholar, Department of Computer Science, Jairams Arts and Science College(Affiliated to Bharathidasan University, Tiruchirappalli), Karur – 639003, Tamilnadu, India.
2B. Mukunthan, Research Supervisor & Assistant Professor, Department of Computer Science, Jairams Arts and Science College(Affiliated to Bharathidasan University, Tiruchirappalli), Karur – 639003,Tamilnadu, India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2422-2430| Volume-8 Issue-10, August 2019 | Retrieval Number: H7095068819/2019©BEIESP | DOI: 10.35940/ijitee.H7095.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Clustering is the process used for partitioning the total dataset into different classes of similar objects. The group contains knowledge about their members and also helps to understand the structure of the dataset very easily. Clustering the bitemporal data is one of the major tasks in data mining since the bitemporal datasets are very large with various attribute counts. Hence the accurate clustering is still challenging tasks. In order to improve the clustering accuracy with less complexity, Kleinberg’s Hyper-richness Bitemporal property based fuzzy c means partition Clustering (KHBP-FCMPC) technique is introduced. The KHBP-FCMPC technique partition the bitemporal dataset into number of possible groups with an improved performance rate based on a distance metric. At first, the ‘c’ numbers of clusters are initialized. The KHBP-FCMPC technique uses the core data point module and authority sector module to minimize the execution time of clustering the data points. Core data point module served as the centroid of the cluster. Each cluster contains one core data point. After that, the distance is computed with the membership function. The authority sector module assigns the data points into the cluster with minimum distance. After that, the centroid is updated and the process iterated until the convergence is met. Finally, the Kleinberg’s Hyper-richness Bitemporal property is applied to verify the total dataset equals the partition of all the data points. This property used to group the entire data points into the cluster with higher accuracy. Experimental evaluation is carried out using a temporal dataset with different factors such as clustering accuracy, false positive rate, time complexity and space complexity with a number of data points. The experimental results show that the proposed KHBP-FCMPC technique increases the bitemporal data clustering accuracy with less false positive rate, time complexity as well as space complexity. Based on the results observations, KHBP-FCMPC technique is more efficient than the state-of-the-art methods.
Keywords: clustering, bitemporal data, Kleinberg’s Hyper-richness Bitemporal property, fuzzy c means partition Clustering, Core data point module, authority sector module.
Scope of the Article: Clustering