Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1, . . .cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this ne...

An approach to clustering is presented that adapts the basic top-down induction of decision trees method towards clustering. To this aim, it employs the principles of instance based learning. The resulting methodology is implemented in the TIC (Top down Induction of Clustering trees) system for first order clustering. The TIC system employs the first order logical decision tree representation o...

The thesis discusses various aspects of learning from a subset of features to another feature subset in the framework of machine learning. We propose feature to feature learning and generalization as a possible model for clustering, as a method to improve feature selection and to incorporate prior knowledge in supervised learning. We also discuss the possible relation of our models to learning ...

Jointly performing semantic and instance segmentation of 3D point cloud remains a challenging task. In this work, novel framework called joint semantic-instance via multi-scale Semantic Association Salient clustering Optimization was proposed to tackle problem. Inspired by the inherent correlation among objects in space, Multi-scale (MSA) module explore constructive effect context information f...

The process of populating an ontology-based system with high-quality and upto-date instance information can be both time consuming and prone to error. In many domains, however, one possible solution to this problem is to automate the instantiation process for a given ontology by searching (mining) the web for the required instance information. The primary challenges facing such a system include...

3:00 PM Session 1 DisClose: Discovering Colossal Closed Itemsets via a Memory Efficient Compact Row-Tree Nurul F. Zulkurnain, David J. Haglin and John A. Keane Triangular Kernel Nearest-Neighbor-based Clustering Algorithm for Discovering True Clusters Aina Musdholifah and Siti Zaiton Mohd Hashim An Improved Genetic Clustering Algorithm for Categorical Data Hongwu Qin, Xiuqin Ma, Tutut Herawan, ...

Discrete analogues to Principal Components Analysis (PCA) are intended to handle discrete or positive-only data, for instance sets of documents. The class of methods is appropriately called multinomial PCA because it replaces the Gaussian in the probabilistic formulation of PCA with a multinomial. Experiments to date, however, have been on small data sets, for instance, from early information r...

The availability of techniques for comparing descriptions has many applications in Artificial Intelligence, ranging from description selection to flexible matching, from instance-based learning to clustering. Due to the complexity of handling First-Order Logic formulæ, where the presence of relations causes various portions of one description to be possibly mapped in different ways onto another...