Similarity metrics for binary cell clustering: How close can we get to state-of-the-art ?

Analysing single-cell RNA sequencing data is becoming an increasingly tedious task as the size of data sets grows. As a proposed solution, recent discoveries suggest that these data sets can be binarized without losing much information. This in turn should allow for memory and time efficient methods of storage and computation. Numerous analyses...

MalPaCA: Malware behaviour analysis using unsupervised machine learning: Comparative analysis of various clustering algorithms on determining the best performance in terms of network behaviour discovery

MalPaCA makes use of unsupervised machine learning to provide malware capability assessment by clustering the temporal behaviour of malware network packet traces. A comparative analysis was performed on various clustering algorithms to determine the best clustering algorithm in terms of network behaviour discovery. The clustering algorithms...

Inferring DFAs from Log Traces Using Community Detection

Large software systems today require increasingly complex models of their execution to aid the analysis of their behavior. Such execution models are impractical to compile by hand, and current approaches to their automated generation are either not generalizable or not scalable enough. This paper addresses this problem with a new approach based...

Molecular Dynamics Simulations of Non-Photochemical Laser-Induced Nucleation: Electrolyte Clustering by Nanoparticle Heating

Non-photochemical laser-induced nucleation (NPLIN) is a process where a crystalline phase is formed out of solution by exposure to a laser beam. In NPLIN, the nucleation probability is strongly dependent on the beam intensity and weakly dependent on the wavelength. NPLIN offers a feasible alternative to energy-intensive industrial...

Automatic Authorship Attribution of Shakespeare’s Plays

Accelerating Cluster Assignment for SeqClu

Clustering is a group of (unsupervised) machine learning algorithms used to categorize data into clusters. The most popular clustering algorithm is k-means clustering. K-means clustering clusters the data into k clusters where a cluster is represented by the mean of the data points called a centroid. Instead of using the mean as a centroid, a...

Predict Radiotherapy Plan Quality

A person with cancer has several treatment options. One of which is radiotherapy. Radiotherapy is treatment of cancer with radiation. To minimize the damage to healthy tissue, radiation is applied from several directions into the body. When treating cancer with radiotherapy, the organs nearby the tumor are at high risk of getting damaged. In the...

Material Tracking System

For a steel company it is advantageous to be able to easily track steel through the production process. At Tata Steel this is currently done with the Material Tracking Table. However, generating this table takes months. Therefore a new system had to be developed. This paper describes the building of such a new system, which generates this...

Clustering Scratch projects by code complexity traits and project traits

Scratch is a popular, visual programming language aimed at children, and is used by teachers and after school code clubs to teach their students about programming. Measuring whether they understand the underlying concepts, however, is a difficult task. In this research, we tried clustering Scratch projects by complexity to help students improve...

Clustering Learning Curves in Machine Learning using K-Means Algorithm: Can patterns be identified amongst learning curves after the application of the K-Means algorithm using point and statistical vectors?

A learning curve can serve as an indicator of the “performance of trained models versus the training set size” [1]. Recent research on learning curve analysis has been carried out within the Learning Curve Database (LCDB) [2] This paper will investigate if there are similarities amongst these curves by clustering those provided by the LCDB. The...

Deciphering Learning Curve Characteristics via K-Means Clustering of Curve Model Parameters

Learning curves illustrate the relationship between the performance of learning algorithms and the increasing volume of training data [1, 2, 3]. While the concept of learning curves is well-established, clustering these curves based on fitting parameters remains an underexplored area. Our study delves into this domain and leverages the Learning...