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A systematic comparative evaluation of biclustering techniques

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Victor Alexandre Padilha
Total Authors: 1
Document type: Master's Dissertation
Press: São Carlos.
Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação
Defense date:
Examining board members:
Ricardo José Gabrielli Barreto Campello; Katti Faceli; David Corrêa Martins Junior; Dilvan de Abreu Moreira
Advisor: Ricardo José Gabrielli Barreto Campello

Data clustering is a fundamental problem in the unsupervised machine learning field, whose objective is to find categories that describe a dataset according to similarities between its objects. In its traditional formulation, we search for partitions or hierarchies of partitions containing clusters such that the objects contained in the same cluster are similar to each other and dissimilar to objects from other clusters according to a similarity or dissimilarity measure that uses all the data attributes in its calculation. So, it is supposed that all clusters are characterized in the same feature space. However, there are several applications where the clusters are characterized only in a subset of the attributes, which could be different from one cluster to another. Different than traditional data clustering algorithms, biclustering algorithms are able to cluster the rows and columns of a data matrix simultaneously, producing biclusters formed with strongly related subsets of objects and subsets of attributes. These algorithms started to draw the scientific communitys attention only after some studies that show their importance for gene expression data analysis. To a lesser degree, biclustering techniques have also been used in other application domains, such as text mining and collaborative filtering in recommendation systems. The problem is that several biclustering algorithms have been proposed in the past recent years with different principles and assumptions, which could result in different outcomes in the same dataset. So, it becomes important to perform comparative studies that could illustrate the behavior and performance of some algorithms. In this thesis, it is presented a comparative study with 17 biclustering algorithms (which are representative of the main categories of algorithms in the literature) which were tested on synthetic and real data collections, with particular emphasis on gene expression data analysis. Several methodologies and experimental evaluation procedures were taken into account during the research, in order to overcome the limitations of previous comparative studies from the literature. Beyond the presented comparison, the comparative methodology developed could be reused to compare other algorithms in the future. (AU)

FAPESP's process: 14/08840-0 - Systematic evaluation of bi-clustering techniques
Grantee:Victor Alexandre Padilha
Support type: Scholarships in Brazil - Master