1,072 research outputs found
Mining Biclusters of Similar Values with Triadic Concept Analysis
Biclustering numerical data became a popular data-mining task in the
beginning of 2000's, especially for analysing gene expression data. A bicluster
reflects a strong association between a subset of objects and a subset of
attributes in a numerical object/attribute data-table. So called biclusters of
similar values can be thought as maximal sub-tables with close values. Only few
methods address a complete, correct and non redundant enumeration of such
patterns, which is a well-known intractable problem, while no formal framework
exists. In this paper, we introduce important links between biclustering and
formal concept analysis. More specifically, we originally show that Triadic
Concept Analysis (TCA), provides a nice mathematical framework for
biclustering. Interestingly, existing algorithms of TCA, that usually apply on
binary data, can be used (directly or with slight modifications) after a
preprocessing step for extracting maximal biclusters of similar values.Comment: Concept Lattices and their Applications (CLA) (2011
A scalable mining of frequent quadratic concepts in d-folksonomies
Folksonomy mining is grasping the interest of web 2.0 community since it
represents the core data of social resource sharing systems. However, a
scrutiny of the related works interested in mining folksonomies unveils that
the time stamp dimension has not been considered. For example, the wealthy
number of works dedicated to mining tri-concepts from folksonomies did not take
into account time dimension. In this paper, we will consider a folksonomy
commonly composed of triples and we shall consider the
time as a new dimension. We motivate our approach by highlighting the battery
of potential applications. Then, we present the foundations for mining
quadri-concepts, provide a formal definition of the problem and introduce a new
efficient algorithm, called QUADRICONS for its solution to allow for mining
folksonomies in time, i.e., d-folksonomies. We also introduce a new closure
operator that splits the induced search space into equivalence classes whose
smallest elements are the quadri-minimal generators. Carried out experiments on
large-scale real-world datasets highlight good performances of our algorithm
Mining localized co-expressed gene patterns from microarray data
Ph.DDOCTOR OF PHILOSOPH
Exploring Constraints Inconsistence for Value Decomposition and Dimension Selection Using Subspace Clustering
Abstract: The datasets which are in the form of object-attribute-time is referred to as threedimensional (3D) data sets. As there are many timestamps in 3D datasets, it is very difficult to cluster. So a subspace clustering method is applied to cluster 3D data sets. Existing algorithms are inadequate to solve this clustering problem. Most of them are not actionable (ability to suggest profitable or beneficial action), and its 3D structure complicates clustering process. To cluster these three-dimensional (3D) data sets a new centroid based concept is introduced in the proposed system called PCA. This PCA framework is introduced to provide excellent performance on financial and stock domain datasets through the unique combination of Singular Value Decomposition, Principle Component Analysis and 3D frequent item set mining.PCA framework prunes the entire search space to identify the significant subspaces and clusters the datasets based on optimal centroid value. This framework acts as the parallelization technique to tackle the space and time complexities
Interactive Data Exploration with Smart Drill-Down
We present {\em smart drill-down}, an operator for interactively exploring a
relational table to discover and summarize "interesting" groups of tuples. Each
group of tuples is described by a {\em rule}. For instance, the rule tells us that there are a thousand tuples with value in the
first column and in the second column (and any value in the third column).
Smart drill-down presents an analyst with a list of rules that together
describe interesting aspects of the table. The analyst can tailor the
definition of interesting, and can interactively apply smart drill-down on an
existing rule to explore that part of the table. We demonstrate that the
underlying optimization problems are {\sc NP-Hard}, and describe an algorithm
for finding the approximately optimal list of rules to display when the user
uses a smart drill-down, and a dynamic sampling scheme for efficiently
interacting with large tables. Finally, we perform experiments on real datasets
on our experimental prototype to demonstrate the usefulness of smart drill-down
and study the performance of our algorithms
A Novel Method for Mining Temporally Dependent Association Rules in Three-Dimensional Microarray Datasets
[[abstract]]Microarray data analysis is a very popular topic of current studies in bioinformatics. Most of the existing methods are focused on clustering-related approaches. However, the relations of genes cannot be generated by clustering mining. Some studies explored association rule mining on microarray, but there is no concrete framework proposed on three-dimensional gene-sample-time microarray datasets yet. In this paper, we proposed a temporal dependency association rule mining method named 3D-TDAR-Mine for three-dimensional analyzing microarray datasets. The mined rules can represent the regulated-relations between genes. Through experimental evaluation, our proposed method can discover the meaningful temporal dependent association rules that are really useful for biologists.[[conferencetype]]國際[[conferencedate]]20101216~20101218[[iscallforpapers]]Y[[conferencelocation]]Tainan, Taiwa
Extending adjacency matrices to 3D with triangles
Social networks are the fabric of society and the subject of frequent visual
analysis. Closed triads represent triangular relationships between three people
in a social network and are significant for understanding inherent
interconnections and influence within the network. The most common methods for
representing social networks (node-link diagrams and adjacency matrices) are
not optimal for understanding triangles. We propose extending the adjacency
matrix form to 3D for better visualization of network triads. We design a 3D
matrix reordering technique and implement an immersive interactive system to
assist in visualizing and analyzing closed triads in social networks. A user
study and usage scenarios demonstrate that our method provides substantial
added value over node-link diagrams in improving the efficiency and accuracy of
manipulating and understanding the social network triads.Comment: 10 pages, 8 figures and 3 table
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