An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering

Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.

Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins

Functional modules can be predicted using genome-wide protein–protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed.

eHMCOKE: an enhanced overlapping clustering algorithm for data analysis

Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) in detecting outliers in datasets makes the algorithm more effective with regards to overlapping clustering. Nevertheless, analysis of the applied MAD positioning was not considered. In this paper, the incorporation of MAD used to detect outliers in the datasets was analyzed to determine the appropriate position in identifying the outlier before applying it in the clustering application. And the assumption of the study was the size of the cluster and cluster that are close to each other can led to a higher runtime performance in terms of overlapping clusters. Therefore, additional parameters such as radius of clusters and distance between clusters are added measurements in the algorithm procedures. Evaluation was done through experimentations using synthetic and real datasets. The performance of the eHMCOKE was evaluated via F1-measure criterion, speed and percentage of improvement. Evaluation results revealed that the eHMCOKE takes less time to discover overlap clusters with an improvement rate of 22% and achieved the best performance of 91.5% accuracy rate via F1-measure in identifying overlapping clusters over the IMCOKE algorithm. These results proved that the eHMCOKE significantly outruns the IMCOKE algorithm on mosts of the test conducted.

Intellectual Humility, Proper Pride, and Proper Concern with Others’ Esteem

This chapter provides accounts of four character traits: intellectual modesty and acceptance of intellectual limitations (which together constitute intellectual humility); proper pride in one’s epistemic achievements and proper concern for one’s intellectual reputation. It argues that these are intellectual virtues. The main difference between humility (as comprising of modesty and of acceptance of limitations) on the one hand, and pride and concern for esteem on the other, lies in the nature of social comparisons on which they are based. Humility relies on appraisals of the worth of one’s qualities that might be gauged by comparing oneself to other people and which are driven by a concern for accuracy. The chapter also makes a case that overlapping clusters of attitudes serving knowledge and value expressive functions are the causal bases of these character traits.

A Density-Based Method for the Identification of Non-Disjoint Clusters With Arbitrary and Non-Spherical Shapes

Overlapping clustering is an important challenge in unsupervised learning applications while it allows for each data object to belong to more than one group. Several clustering methods were proposed to deal with this requirement by using several usual clustering approaches. Although the ability of these methods to detect non-disjoint partitioning, they fail when data contain groups with arbitrary and non-spherical shapes. We propose in this work a new density based overlapping clustering method, referred to as OC-DD, which is able to detect overlapping clusters even having non-spherical and complex shapes. The proposed method is based on the density and distances to detect dense regions in data while allowing for some data objects to belong to more than one group.Experiments performed on articial and real multi-labeled datasets have shown the effectiveness of the proposed method compared to the existing ones.

UnFEAR

We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses unsupervised representation learning and a novel mode contrastive autoencoder to group episodes into time-invariant non-overlapping clusters, each of which could be identified with a different regime. The likelihood that a country may experience an econmic crisis could be set equal to its cluster crisis frequency. Moreover, unFEAR could serve as a first step towards developing cluster-specific crisis prediction models tailored to each crisis regime.

Detection of functional deterioration in glaucoma by trend analysis using comprehensive overlapping clusters of locations

Detecting rapid visual field deterioration is crucial for individuals with glaucoma. Cluster trend analysis detects visual field deterioration with higher sensitivity than global analyses by using predefined non-overlapping subsets of visual field locations. However, it may miss small defects that straddle cluster borders. This study introduces a comprehensive set of overlapping clusters, and assesses whether this further improves progression detection. Clusters were defined as locations from where ganglion cell axons enter the optic nerve head within a θ° wide sector, centered at 1º intervals, for various θ. Deterioration in eyes with or at risk of glaucomatous visual field loss was “detected” if ≥ Nθ clusters had deteriorated with p < pCluster, chosen empirically to give 95% specificity based on permuting the series. Nθ was chosen to minimize the time to detect subsequently-confirmed deterioration in ≥ 1/3rd of eyes. Times to detect deterioration were compared using Cox survival models. Biannual series were available for 422 eyes of 214 participants. Predefined non-overlapping clusters detected subsequently-confirmed change in ≥ 1/3rd of eyes in 3.41 years (95% confidence interval 2.75–5.48 years). After equalizing specificity, no criteria based on comprehensive overlapping clusters detected deterioration significantly sooner. The quickest was 3.13 years (2.69–4.65) for θ° = 20° and Nθ = 25, but the comparison with non-overlapping clusters had p = 0.672. Any improvement in sensitivity for detecting deterioration when using a comprehensive set of overlapping clusters was negated by the need to maintain equal specificity. The existing cluster trend analysis using predefined non-overlapping clusters provides a useful tool for monitoring visual field progression.