Density-based clustering with constraints
نویسندگان
چکیده
منابع مشابه
Instance-Level Constraints in Density-Based Clustering
Clustering data into meaningful groups is one of most important tasks of both artificial intelligence and data mining. In general, clustering methods are considered unsupervised. However, in recent years, so-named constraints become more popular as means of incorporating additional knowledge into clustering algorithms. Over the last years, a number of clustering algorithms employing different t...
متن کاملGraph-Based Clustering with Constraints
A common way to add background knowledge to the clustering algorithms is by adding constraints. Though there had been some algorithms that incorporate constraints into the clustering process, not much focus was given to the topic of graph-based clustering with constraints. In this paper, we propose a constrained graph-based clustering method and argue that adding constraints in distance functio...
متن کاملSOM based clustering with instance-level constraints
This paper describes a new topological map dedicated to clustering under instance-level constraints. In general, traditional clustering is used in an unsupervised manner. However, in some cases, background information about the problem domain is available or imposed in the form of constraints, in addition to data instances. In this context, we modify the popular SOM algorithm to take these cons...
متن کاملModel-based Clustering With Probabilistic Constraints
The problem of clustering with constraints is receiving increasing attention. Many existing algorithms assume the specified constraints are correct and consistent. We take a new approach and model the uncertainty of constraints in a principled manner by treating the constraints as random variables. The effect of specified constraints on a subset of points is propagated to other data points by b...
متن کاملC-NBC: Neighborhood-Based Clustering with Constraints
Clustering is one of most important methods of data mining. It is used to identify unknown yet interesting and useful patterns or trends in datasets. There are different types of clustering algorithms such as partitioning, hierarchical, grid and density-based. In general, clustering methods are considered unsupervised, however, in recent years the new branch of clustering algorithms has emerged...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computer Science and Information Systems
سال: 2019
ISSN: 1820-0214,2406-1018
DOI: 10.2298/csis180601007l