We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the clusters. The algorithms work by maximizing the dependence between the taxonomy and the original data. The resulting taxonomy is a more informative visualization of complex data than simple clustering; in addition, tak...

We introduce Propositionalized Attribute Taxonomy guided Decision Tree Learner (PAT-DTL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact decision trees. Since taxonomies are unavailable in most domains, we also introduce Propositionalized Attribute Taxonomy Learner (PAT-Learner) that automatically constructs taxono...

Taxonomy(general), the practice and science of classification of things or concepts, including the principles that underlie such classification. Economic taxonomy, a system of classification for economic activity. The main objective of the study was to find whether financial ratios affect the performance of the Agricultural Machinery companies in Iran. A firm performance evaluation and its comp...

We explore in this paper the efficient clustering of item data. Different from those of the traditional data, the features of item data are known to be of high dimensionality and sparsity. In view of the features of item data, we devise in this paper a novel measurement, called the associationtaxonomy similarity, and utilize this measurement to perform the clustering. With this association-taxo...

We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the clusters. The algorithms work by maximizing the dependence between the taxonomy and the original data. The resulting taxonomy is a more informative visualization of complex data than simple clustering; in addition, tak...

Formal Concept Analysis (FCA) considers attributes as a non-ordered set. This is appropriate when the data set is not structured. When an attribute taxonomy exists, existing techniques produce a completed context with all attributes deduced from the taxonomy. Usual algorithms can then be applied on the completed context for finding frequent concepts, but the results systematically contain redun...

Fault detection is vital to wireless sensor networks since node death is a typical fault. One of the central challenges is to design a detection algorithm which has good performance in energy efficiency. In this paper, we propose an intelligent fault detection algorithm based on numerical taxonomy. Firstly, all nodes are divided into clusters according to their geographical distribution. Beside...

In this paper we present the ideas and algorithms developed around our KeyGen Web Taxonomy Annotation engine. KeyGen annotates the Open Directory Project, also known as Dmoz, with meaningful and previously unknown keywords by utilizing domain knowledge extracted from the WWW. We present two algorithms: i) The PageParse Algorithm, which efficiently extracts keywords from Web Taxonomies using a c...

In this paper, we introduce a set of taxonomies that hierarchically organize and specify algorithms associated with hyperspectral unmixing. Our motivation is to collectively organize and relate algorithms in order to assess the current state-of-the-art in the field and to facilitate objective comparisons between methods. The hyperspectral sensing community is populated by investigators with dis...