Semi-supervised approaches have proven to be effective in clustering tasks. They allow user input, thus improving the quality of the clustering obtained, while maintaining a controllable level of user intervention. Despite being an important class of algorithms, hierarchical clustering has been little explored in semisupervised solutions. In this report, we address the problem of semi-supervise...

MultiDendrograms is a Java-written application that computes agglomerative hierarchical clusterings of data. Starting from a distances (or weights) matrix, MultiDendrograms is able to calculate its dendrograms using the most common agglomerative hierarchical clustering methods. The application implements a variable-group algorithm that solves the non-uniqueness problem found in the standard pai...

Privatization of municipal housing in Poland has led to the emergence public-private condominiums. The aim study was investigate ownership structures common property management entities such condominiums Poland. intended investigation conducted on sample 30 largest urban municipalities located Warmia and Mazury province. empirical data collected by questionnaire interviews using public informat...

spatial distribution of thermal regions is dependent on local factors and circulation patterns in long terms. recognition of spatial distribution of temperature in geographical regions could be help on planning and environmental policies. the aim of this paper is recognition and detachment of thermal regions in iran. for this object, maximum daily temperature data have been provided using 620 s...

Community detection in networks based on modularity maximization is currently done with hierarchical divisive or agglomerative as well as partitioning heuristics, hybrids, and, in a few papers, exact algorithms. We consider here the case of hierarchical networks in which communities should be detected and propose a divisive heuristic which is locally optimal in the sense that each of the succes...

In this paper we introduce a general framework for hierarchical clustering that deals with both static and dynamic data sets. From this framework, different hierarchical agglomerative algorithms can be obtained, by specifying an inter-cluster similarity measure, a subgraph of the β-similarity graph, and a cover algorithm. A new clustering algorithm called Hierarchical Compact Algorithm and its ...

CLOPE (Clustering with sLOPE) is a simple and fast histogram-based clustering algorithm for categorical data. However, given the same data set with the same input parameter, the clustering results by this algorithm would possibly be different if the transactions are input in a different sequence. In this paper, a hierarchical clustering framework is proposed as an extension of CLOPE to generate...

Hierarchical Clustering is a procedure of cluster analysis which aims to construct a hierarchy of clusters. There are two kinds of hierarchical clustering i.e. Agglomerative, which is a bottom – up approach, where all the observations start in its own cluster, and pairs of clusters are merged moving up the hierarchy, and the other one is divisive, which is a top down approach, where each observ...