We propose a novel ensemble classification technique called the Nearest Instance Centroid Estimation (NICE) LDA algorithm. Our algorithm (inspired from NICE KLMS) performs a combination of two weak classifiers threshold based clustering and linear discriminant classification to achieve stateof-the-art results on various high dimensional UCI datasets. We discuss the important ways in which our m...

We propose an efficient nonparametric missing value imputation method based on clustering, called CMI (Clustering-based Missing value Imputation), for dealing with missing values in target attributes. In our approach, we impute the missing values of an instance A with plausible values that are generated from the data in the instances which do not contain missing values and are most similar to t...

A novel rst order clustering system, called C 0.5, is presented. It inherits its logical decision tree formalism from the TILDE system, but instead of using class information to guide the search, it employs the principles of instance based learning in order to perform clustering. Various experiments are discussed, which show the promise of the approach.

Mean Shift today, is widely used for mode detection and clustering. The technique though, is challenged in practice due to assumptions of isotropicity and homoscedasticity. We present an adaptive Mean Shift methodology that allows for full anisotropic clustering, through unsupervised local bandwidth selection. The bandwidth matrices evolve naturally, adapting locally through agglomeration, and ...

“The statistical problem of testing cluster validity is essentially unsolved” [5]. We translate the issue of gaining credibility on the output of un-supervised learning algorithms to the supervised learning case. We introduce a notion of instance easiness to supervised learning and link the validity of a clustering to how its output constitutes an easy instance for supervised learning. Our noti...

This paper investigates the new problem of automatic sense induction for instance names using automatically extracted attribute sets. Several clustering strategies and data sources are described and evaluated. We also discuss the drawbacks of the evaluation metrics commonly used in similar clustering tasks. The results show improvements in most metrics with respect to the baselines, especially ...

Efficient Algorithms for Clustering and Classifying High Dimensional Text and Discretized Data using Interesting Patterns Hassan H. Malik Recent advances in data mining allow for exploiting patterns as the primary means for clustering and classifying large collections of data. In this thesis, we present three advances in pattern-based clustering technology, an advance in semi-supervised pattern...

In this paper, we propose to adapt the batch version of selforganizing map (SOM) to background information in clustering task. It deals with constrained clustering with SOM in a deterministic paradigm. In this context we adapt the appropriate topological clustering to pairwise instance level constraints with the study of their informativeness and coherence properties for measuring their utility...

Recent works in unsupervised learning have emphasized the need to understand a new trend in algorithmic design, which is to influence the clustering via weights on the instance points. In this paper, we handle clustering as a constrained minimization of a Bregman divergence. Theoretical results show benefits resembling those of boosting algorithms, and bring new modified weighted versions of cl...

Confounded information is an objective fact when using multi-instance learning (MIL) to classify bags of instances, which may be inherited by MIL embedding methods and lead questionable bag label prediction. To respond this problem, we propose the with deconfounded instance-level prediction algorithm. Unlike traditional embedding-based strategies, design a optimization goal maximize distinction...