SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting

Sequence clustering in a streaming environment is challenging because it is computationally expensive, and the sequences may evolve over time. K-medoids or Partitioning Around Medoids (PAM) is commonly used to cluster sequences since it supports alignment-based distances, and the k-centers being actual data items helps with cluster...

Finding Similarities between Measures in Scanned Music Scores

The field of Optical Music Recognition has been making progress in the past decades to automate the process of transcribing music scores into computer-readable formats, but its results are still far from being generally applicable. Some research effort has focused on incorporating crowdsourcing techniques into this field to check and correct...

Cold start is coming: How to approximate the optimal set of initial prototypes for clustering sequence data online

Clustering data is a classic topic in the academic community and in the industry. It is by and large one of the most popular unsupervised classification techniques. It is fast and flexible as it can accommodate all kinds of data when a suitable similarity metric is found. SeqClu is an online k-medoids prototype based clustering algorithm...

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...

SeqClu-PV: An extension of online K-medoids to efficiently cluster sequences real-time

Real-time sequence clustering is the problem of clustering an infinite stream of sequences in real time with limited memory. A variant of the k-medoids algorithm called <i>SeqClu </i>is the suggested approach, representing a cluster with <i>p </i>most representative sequences of the cluster, called prototypes, to solve the problem of maintaining...

Dynamic PTDF Implementation in the Market Model

This master thesis aims at developing the methodology to form dynamic PTDF families, for the implementation into the market model. The main procedures to create a dynamic PTDF family are developed and presented with a study case (See Chapter 4). By fitting data from the market model into the data mining tools, typical scenarios of the time...