Computing iceberg concept lattices with Titanic

International audienceWe introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suited for analyzing very large databases. They also serve as a condensed representation of frequent itemsets, as starting point for computing bases of association rules, and as a visualization method for association rules. Iceberg concept lattices are based on the theory of Formal Concept Analysis, a mathematical theory with applications in data analysis, information retrieval, and knowledge discovery. We present a new algorithm called TITANIC for computing (iceberg) concept lattices. It is based on data mining techniques with a level-wise approach. In fact, TITANIC can be used for a more general problem: Computing arbitrary closure systems when the closure operator comes along with a so-called weight function. The use of weight functions for computing closure systems has not been discussed in the literature up to now. Applications providing such a weight function include association rule mining, functional dependencies in databases, conceptual clustering, and ontology engineering. The algorithm is experimentally evaluated and compared with Ganter's Next-Closure algorithm. The evaluation shows an important gain in efficiency, especially for weakly correlated data

Motif Discovery through Predictive Modeling of Gene Regulation

We present MEDUSA, an integrative method for learning motif models of transcription factor binding sites by incorporating promoter sequence and gene expression data. We use a modern large-margin machine learning approach, based on boosting, to enable feature selection from the high-dimensional search space of candidate binding sequences while avoiding overfitting. At each iteration of the algorithm, MEDUSA builds a motif model whose presence in the promoter region of a gene, coupled with activity of a regulator in an experiment, is predictive of differential expression. In this way, we learn motifs that are functional and predictive of regulatory response rather than motifs that are simply overrepresented in promoter sequences. Moreover, MEDUSA produces a model of the transcriptional control logic that can predict the expression of any gene in the organism, given the sequence of the promoter region of the target gene and the expression state of a set of known or putative transcription factors and signaling molecules. Each motif model is either a $k$-length sequence, a dimer, or a PSSM that is built by agglomerative probabilistic clustering of sequences with similar boosting loss. By applying MEDUSA to a set of environmental stress response expression data in yeast, we learn motifs whose ability to predict differential expression of target genes outperforms motifs from the TRANSFAC dataset and from a previously published candidate set of PSSMs. We also show that MEDUSA retrieves many experimentally confirmed binding sites associated with environmental stress response from the literature.Comment: RECOMB 200

Ensemble rapid centroid estimation : a semi-stochastic consensus particle swarm approach for large scale cluster optimization

University of Technology Sydney. Faculty of Engineering and Information Technology.This thesis details rigorous theoretical and empirical analyses on the related works in the clustering literature based on the Particle Swarm Optimization (PSO) principles. In particular, we detail the discovery of disadvantages in Van Der Merwe - Engelbrecht’s PSO clustering, Cohen - de Castro Particle Swarm Clustering (PSC), Szabo’s modified PSC (mPSC) and Szabo’s Fuzzy PSC (FPSC). We validate, both theoretically and empirically, that Van Der Merwe - Engelbrecht’s PSO clustering algorithm is not significantly better than the conventional k-means. We propose that under random initialization, the performance of their proposed algorithm diminishes exponentially as the number of classes or dimensions increase. We unravel that the PSC, mPSC, and FPSC algorithms suffer from significant complexity issues which do not translate into performance. Their cognitive and social parameters have negligible effect to convergence and the algorithms generalize to the k-means, retaining all of its characteristics including the most severe: the curse of initial position. Furthermore we observe that the three algorithms, although proposed under varying names and time frames, behave similarly to the original PSC. This thesis analyzes, both theoretically and empirically, the strengths and limitations of our proposed semi-stochastic particle swarm clustering algorithm, Rapid Centroid Estimation (RCE), self-evolutionary Ensemble RCE (ERCE), and Consensus Engrams, which are developed mainly to address the fundamental issues in PSO Clustering and the PSC families. The algorithms extend the scalability, applicability, and reliability of earlier approaches to handle large-scale non-convex cluster optimization in quasilinear complexity in both time and space. This thesis establishes the fundamentals, much surpassing those outlined in our published manuscripts