5 research outputs found
Clustering and its Application in Requirements Engineering
Large scale software systems challenge almost every activity in the software development life-cycle, including tasks related to eliciting, analyzing, and specifying requirements. Fortunately many of these complexities can be addressed through clustering the requirements in order to create abstractions that are meaningful to human stakeholders. For example, the requirements elicitation process can be supported through dynamically clustering incoming stakeholders’ requests into themes. Cross-cutting concerns, which have a significant impact on the architectural design, can be identified through the use of fuzzy clustering techniques and metrics designed to detect when a theme cross-cuts the dominant decomposition of the system. Finally, traceability techniques, required in critical software projects by many regulatory bodies, can be automated and enhanced by the use of cluster-based information retrieval methods. Unfortunately, despite a significant body of work describing document clustering techniques, there is almost no prior work which directly addresses the challenges, constraints, and nuances of requirements clustering. As a result, the effectiveness of software engineering tools and processes that depend on requirements clustering is severely limited. This report directly addresses the problem of clustering requirements through surveying standard clustering techniques and discussing their application to the requirements clustering process
Automatic region-of-interest extraction in low depth-of-field images
PhD ThesisAutomatic extraction of focused regions from images with low depth-of-field
(DOF) is a problem without an efficient solution yet. The capability of
extracting focused regions can help to bridge the semantic gap by integrating
image regions which are meaningfully relevant and generally do not exhibit
uniform visual characteristics. There exist two main difficulties for extracting
focused regions from low DOF images using high-frequency based techniques:
computational complexity and performance.
A novel unsupervised segmentation approach based on ensemble clustering is
proposed to extract the focused regions from low DOF images in two stages.
The first stage is to cluster image blocks in a joint contrast-energy feature space
into three constituent groups. To achieve this, we make use of a normal
mixture-based model along with standard expectation-maximization (EM)
algorithm at two consecutive levels of block size. To avoid the common
problem of local optima experienced in many models, an ensemble EM
clustering algorithm is proposed. As a result, relevant blocks, i.e., block-based
region-of-interest (ROI), closely conforming to image objects are extracted.
In stage two, two different approaches have been developed to extract
pixel-based ROI. In the first approach, a binary saliency map is constructed
from the relevant blocks at the pixel level, which is based on difference of
Gaussian (DOG) and binarization methods. Then, a set of morphological
operations is employed to create the pixel-based ROI from the map.
Experimental results demonstrate that the proposed approach achieves an
average segmentation performance of 91.3% and is computationally 3 times
faster than the best existing approach. In the second approach, a minimal graph
cut is constructed by using the max-flow method and also by using
object/background seeds provided by the ensemble clustering algorithm.
Experimental results demonstrate an average segmentation performance of 91.7%
and approximately 50% reduction of the average computational time by the
proposed colour based approach compared with existing unsupervised
approaches
Voting-Based Consensus of Data Partitions
Over the past few years, there has been a renewed interest in the consensus
problem for ensembles of partitions. Recent work is primarily motivated by the
developments in the area of combining multiple supervised learners. Unlike the
consensus of supervised classifications, the consensus of data partitions is a
challenging problem due to the lack of globally defined cluster labels and to
the inherent difficulty of data clustering as an unsupervised learning problem.
Moreover, the true number of clusters may be unknown. A fundamental goal of
consensus methods for partitions is to obtain an optimal summary of an ensemble
and to discover a cluster structure with accuracy and robustness exceeding those
of the individual ensemble partitions.
The quality of the consensus partitions highly depends on the ensemble
generation mechanism and on the suitability of the consensus method for
combining the generated ensemble. Typically, consensus methods derive an
ensemble representation that is used as the basis for extracting the consensus
partition. Most ensemble representations circumvent the labeling problem. On
the other hand, voting-based methods establish direct parallels with consensus
methods for supervised classifications, by seeking an optimal relabeling of the
ensemble partitions and deriving an ensemble representation consisting of a
central aggregated partition. An important element of the voting-based
aggregation problem is the pairwise relabeling of an ensemble partition with
respect to a representative partition of the ensemble, which is refered to here
as the voting problem. The voting problem is commonly formulated as a weighted
bipartite matching problem.
In this dissertation, a general theoretical framework for the voting problem as
a multi-response regression problem is proposed. The problem is formulated as
seeking to estimate the uncertainties associated with the assignments of the
objects to the representative clusters, given their assignments to the clusters
of an ensemble partition. A new voting scheme, referred to as cumulative voting,
is derived as a special instance of the proposed regression formulation
corresponding to fitting a linear model by least squares estimation. The
proposed formulation reveals the close relationships between the underlying loss
functions of the cumulative voting and bipartite matching schemes. A useful
feature of the proposed framework is that it can be applied to model substantial
variability between partitions, such as a variable number of clusters.
A general aggregation algorithm with variants corresponding to
cumulative voting and bipartite matching is applied and a simulation-based
analysis is presented to compare the suitability of each scheme to different
ensemble generation mechanisms. The bipartite matching is found to be more
suitable than cumulative voting for a particular generation model, whereby each
ensemble partition is generated as a noisy permutation of an underlying
labeling, according to a probability of error. For ensembles with a variable
number of clusters, it is proposed that the aggregated partition be viewed as an
estimated distributional representation of the ensemble, on the basis of which,
a criterion may be defined to seek an optimally compressed consensus partition.
The properties and features of the proposed cumulative voting scheme are
studied. In particular, the relationship between cumulative voting and the
well-known co-association matrix is highlighted. Furthermore, an adaptive
aggregation algorithm that is suited for the cumulative voting scheme is
proposed. The algorithm aims at selecting the initial reference partition and
the aggregation sequence of the ensemble partitions the loss of mutual
information associated with the aggregated partition is minimized. In order to
subsequently extract the final consensus partition, an efficient agglomerative
algorithm is developed. The algorithm merges the aggregated clusters such that
the maximum amount of information is preserved. Furthermore, it allows the
optimal number of consensus clusters to be estimated.
An empirical study using several artificial and real-world datasets demonstrates
that the proposed cumulative voting scheme leads to discovering substantially
more accurate consensus partitions compared to bipartite matching, in the case
of ensembles with a relatively large or a variable number of clusters. Compared
to other recent consensus methods, the proposed method is found to be comparable
with or better than the best performing methods. Moreover, accurate estimates of
the true number of clusters are often achieved using cumulative voting, whereas
consistently poor estimates are achieved based on bipartite matching. The
empirical evidence demonstrates that the bipartite matching scheme is not
suitable for these types of ensembles
Combining partitions by probabilistic label aggregation
Data clustering represents an important tool in exploratory data analysis. The lack of objective criteria render model selection as well as the identification of robust solutions particularly difficult. The use of a stability assessment and the combination of multiple clustering solutions represents an important ingredient to achieve the goal of finding useful partitions. In this work, we propose a novel way of combining multiple clustering solutions for both, hard and soft partitions: the approach is based on modeling the probability that two objects are grouped together. An efficient EM optimization strategy is employed in order to estimate the model parameters. Our proposal can also be extended in order to emphasize the signal more strongly by weighting individual base clustering solutions according to their consistency with the prediction for previously unseen objects. In addition to that, the probabilistic model supports an outof-sample extension that (i) makes it possible to assign previously unseen objects to classes of the combined solution and (ii) renders the efficient aggregation of solutions possible. In this work, we also shed some light on the usefulness of such combination approaches. In the experimental result section, we demonstrate the competitive performance of our proposal in comparison with other recently proposed methods for combining multiple classifications of a finite data set