Browsing by Author "Ishaq, Waqar"

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Capturing the Data Similarity Among Organizations of Same Nature
(Kadir Has Üniversitesi, 2021) Ishaq, Waqar; Şenol, Habib
The vertical collaborative clustering aims to unravel the hidden structure of data (similarity) among different sites, which will help data owners to make a smart decision without sharing actual data. For example, various hospitals located in different regions want to investigate the structure of common disease among people of different populations to identify latent causes without sharing actual data with other hospitals. Similarly, a chain of regional educational institutions wants to evaluate their students' performance belonging to different regions based on common latent constructs. The available methods used for finding hidden structures are complicated and biased to perform collaboration in measuring similarity among multiple sites. In this dissertation, the author proposed two approaches of vertical collaborative clustering, namely (1) Vertical Collaborative Clustering Model (2) Vertical Collaborative Clustering based on Bit-Plane Slicing, with superior accuracy over the state of the art approaches. The Vertical Collaborative Clustering Model (V CCM) manages the collaboration among multiple data sites using Self-Organizing Map (SOM). It includes standard procedure and tuning of the exchanged information in specific proportionality to augment the learning process of the clustering via collaboration. Moreover, the VCCM unravels hidden information without compromising the data confidentiality. The aim of the model is to set an ideal environment for the collaboration process among multiple sites. The VCCM is evaluated by purity measurement, using four datasets (Iris, Geyser, Cancer and Waveform). The findings of this study show the significance of the VCCM by comparing the collaborative results with the local results using purity measurement. The VCCM unlocks possible reasons determining impact of collaboration based on related and unrelated patterns. The results demonstrate that the proposed VCCM improves local learning by collaboration and also helps the data owner to make better decisions on the clustering. Additionally, the results obtained have better accuracy than the existing approaches. The proposed Vertical Collaborative Clustering based on Bit-Plane Slicing (VCCBPS) is simple and unique approach with improved accuracy, manages collaboration among various data sites. The VCC-BPS transforms data from input space to code space, capturing maximum similarity locally and collaboratively at a particular bit plane. The findings of this study highlight the significance of those particular bits which fit the model in correctly classifying clusters locally and collaboratively. Thenceforth, the data owner appraises local and collaborative results to reach a better decision. The VCC-BPS is validated by Geyser, Skin and Iris datasets and its results are compared with the composite dataset. It is found that the VCCBPS outperforms existing solutions with improved accuracy in term of purity and Davies-Bouldin index to manage collaboration among different data sites. It also performs data compression by representing a large number of observations with a small number of data symbols. Keywords: Collaborative clustering, Collaboration, Vertical collaborative clustering, Cluster combination, Purity measurement, Similarity measurement
Citation Count: 1
Dark Patches in Clustering
(IEEE, 2017) Ishaq, Waqar; Büyükkaya, Eliya
This survey highlights issues in clustering which hinder in achieving optimal solution or generates inconsistent outputs. We called such malignancies as dark patches. We focus on the issues relating to clustering rather than concepts and techniques of clustering. For better insight into the issues of clustering we categorize dark patches into three classes and then compare various clustering methods to analyze distributed datasets with respect to classes of dark patches rather than conventional way of comparison by performance and accuracy criteria because performance and accuracy may provide misleading conclusions due to lack of labeled data in unsupervised learning. To the best of our knowledge this prime feature makes our survey paper unique from other clustering survey papers.
Citation Count: 0
Vcc-Bps: Vertical Collaborative Clustering Using Bit Plane Slicing
(PUBLIC LIBRARY SCIENCE, 2021-01) Ishaq, Waqar; Büyükkaya, Eliya; Ali, Mushtaq; Khan, Zakir
The vertical collaborative clustering aims to unravel the hidden structure of data (similarity) among different sites, which will help data owners to make a smart decision without sharing actual data. For example, various hospitals located in different regions want to investigate the structure of common disease among people of different populations to identify latent causes without sharing actual data with other hospitals. Similarly, a chain of regional educational institutions wants to evaluate their students' performance belonging to different regions based on common latent constructs. The available methods used for finding hidden structures are complicated and biased to perform collaboration in measuring similarity among multiple sites. This study proposes vertical collaborative clustering using a bit plane slicing approach (VCC-BPS), which is simple and unique with improved accuracy, manages collaboration among various data sites. The VCC-BPS transforms data from input space to code space, capturing maximum similarity locally and collaboratively at a particular bit plane. The findings of this study highlight the significance of those particular bits which fit the model in correctly classifying class labels locally and collaboratively. Thenceforth, the data owner appraises local and collaborative results to reach a better decision. The VCC-BPS is validated by Geyser, Skin and Iris datasets and its results are compared with the composite dataset. It is found that the VCC-BPS outperforms existing solutions with improved accuracy in term of purity and Davies-Boulding index to manage collaboration among different data sites. It also performs data compression by representing a large number of observations with a small number of data symbols.