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...

Abstract This study adopts a corpus-based behavioral profile approach, combining multifactorial usage-feature analysis with frequency-based quantitative analysis, to investigate the diachronic semasiological variation of Mandarin Chinese temperature term 热 re ‘hot’. The result shows dynamic profile, i.e., both usage patterns and structural weight senses have been constantly shifting. semasiolog...

In most cases where clustering of data is desirable, the underlying data distribution to be clustered is unconstrained. However clustering of site types in a discretely structured linear array, as is often desired in studies of linear sequences such as DNA, RNA or proteins, represents a problem where data points are not necessarily exchangeable and are directionally constrained within the array...

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...

Relational data mining deals with datasets containing multiple types of objects and relationships that are presented in relational formats, e.g. relational databases that have multiple tables. This paper proposes a propositional hierarchical model-based method for clustering relational data. We first define an object-relational star schema to model composite objects, and present a method of fla...

This paper proposes new clustering criteria for distinguishing Saccharomyces cerevisiae (yeast) strains using their spectrometric signature. These criteria are introduced in an agglomerative hierarchical clustering context, and consist of: (a) minimizing the total volume of clusters, as given by their respective convex hulls; and, (b) minimizing the global variance in cluster directionality. Th...

In this paper, we describe our KBP Entity Lining system at TAC 2012. Our system consists of three modules. 1) Query expansion and candidate entity selection module. In this module, we identify all the possible entities for an entity mention through a variety knowledge sources. 2) Entity disambiguation module. In this module, we use a maximum margin approach to rank the candidate entity. 3) NIL ...