Correlation Clustering with Overlaps by Fakhereldine, Amin

“…Moreover, we allow the new vertex splitting operation, which allows the resulting clusters to overlap. In other words, data elements (or vertices) will be allowed to be members in more than one cluster instead of limiting them to only one single cluster, as in classical clustering methods. …”
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Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering by Saadia Jamil (22045946)

“…Finally, this study proposed a novel multi-view learning framework that analyzes multi-source data and generates fine clusters efficiently.…”

Intelligent Fusion of Deep Features for Improved Waste Classification by Kashif Ahmad (12592762)

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NEW ALGORITHMS FOR SOLVING THE FUZZY CLUSTERING PROBLEM by Kamel, M.S.

“…Two new algorithms for fuzzy clustering are presented. Convergence of the proposed algorithms is proved. …”
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Computational Experience On Four Algorithms For The Hard Clustering Problem by AlSultan, K.S.

“…In this paper, we study the four algorithms and compare their computational performance for the clustering problem. …”
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A new approach to record clustering for large databases. (c1997) by Makhoulian, Raffi H.

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Artificial intelligence-based methods for fusion of electronic health records and imaging data by Farida Mohsen (16994682)

“…In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. …”

An enhanced k-means clustering algorithm for pattern discovery in healthcare data by Haraty, Ramzi A.

“…Data mining approaches offer the methodology and technology to transform these heterogeneous data into meaningful information for decision making. This paper studies data mining applications in healthcare. Mainly, we study k-means clustering algorithms on large datasets and present an enhancement to k-means clustering, which requires k or a lesser number of passes to a dataset. …”
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On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network by Nasru Minallah (9427338)

Cluster-based anonymity model and algorithm for 1:1 dataset with a single sensitive attribute using machine learning technique by J. Jayapradha (21323774)

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks by Umesh Kumar Lilhore (17727684)

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Correlation Clustering via 2-Club Clustering with Vertex Splitting by Thoumi, Sergio

“…In the realm of graph theory, correlation cluster is studied as a graph modification problem known under "Cluster Editing." …”
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Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

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Parallel scatter search algorithms for exam timetabling. (c2009) by Haidar, Ghia Abdullah Sleiman

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Bird’s Eye View feature selection for high-dimensional data by Samir Brahim Belhaouari (16855434)

“…This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”

A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities by Mahdi Mokhtarzadeh (11593310)

“…The results indicate that KNSGA-II is superior to other algorithms. Also, a case study in Iran is implemented and the related results are analyzed.…”

On the complexity of multi-parameterized cluster editing by Abu-Khzam, Faisal

“…In other words, Cluster Editing can be solved efficiently when the number of false positives/negatives per single data element is expected to be small compared to the minimum cluster size. …”
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A TABU SEARCH APPROACH TO THE CLUSTERING PROBLEM by AlSultan, K.S.

“…The problem is a nonconvex program that has many local minima. It has been studied by many researchers and the most well-known algorithm for solving it is the k-means algorithm. …”
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Decision-level Gait Fusion for Human Identification at a Distance by Al-Tayyan, Amer

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Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques by Fares, Samar

“…The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. …”
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