Density-Based Clustering with Geographical Background Constraints Using a Semantic Expression Model
نویسندگان
چکیده
منابع مشابه
Density-Based Clustering with Geographical Background Constraints Using a Semantic Expression Model
A semantics-based method for density-based clustering with constraints imposed by geographical background knowledge is proposed. In this paper, we apply an ontological approach to the DBSCAN (Density-Based Geospatial Clustering of Applications with Noise) algorithm in the form of knowledge representation for constraint clustering. When used in the process of clustering geographic information, s...
متن کاملClustering Gene Expression Data using a Regulation based Density Clustering
We present a density based method for clustering gene expression data using a two-objective function. The method uses regulation information as well as a suitable dissimilarity measure to cluster genes into regions of higher density separated by sparser regions. The method has been tested on five benchmark microarray datasets and found to perform well in terms of homogeneity and z-score measures.
متن کاملMonothetic divisive clustering with geographical constraints
DIVCLUS-T is a descendant hierarchical clustering algorithm based on a monothetic bipartitional approach allowing the dendrogram of the hierarchy to be read as a decision tree. We propose in this paper a new version of this method called C-DIVCLUS-T which is able to take contiguity constraints into account. We apply C-DIVCLUS-T to hydrological areas described by agricultural and environmental v...
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 کاملInstance-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 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: ISPRS International Journal of Geo-Information
سال: 2016
ISSN: 2220-9964
DOI: 10.3390/ijgi5050072