In this paper, we propose a true unsupervised method to partition large-size images, where
the number of classes, training samples, and other a priori information is not known. Thus …

Entity Matching (EM), which aims to identify all pairs of records referring to the same real-
world entity from relational tables, is one of the most important tasks in real-world data …

Data lakes are typically lightly curated and as such prone to data quality problems and
inconsistencies. In particular, duplicate tables are common in most repositories. The goal of …

We propose extensions to Hierarchical Agglomerative Clustering (HAC) to match and cluster
entities from multiple sources that can be either duplicate-free or dirty. The proposed …

Data lakes are typically lightly curated and as such prone to data quality problems and
inconsistencies. In particular, duplicate tables are common in most repositories. The goal of …

It is important for quality prediction and monitoring to ensure the safe operation of the
process. When constructing a prediction model, it is crucial to choose appropriate input …

Cluster repair methods aim to determine errors in clusters and modify them so that each
cluster consists of records representing the same entity. Current cluster repair …

Entity resolution is essential for data integration, facilitating analytics and insights from
complex systems. Multi-source and incremental entity resolution address the challenges of …

Record linkage is the task of identifying records from different databases that refer to the
same real-world entity. This task is an essential component of data integration to facilitate …

Entity resolution is a critical task in enhancing data quality and ensuring the reliability of data
analytics, as it involves identifying distinct records in a dataset that correspond to the same …