Link-based Clustering Research Articles

Mapping the landscape of spatial literacy research: Bibliometric analysis

The study aims to provide a comprehensive overview of spatial literacy research through a bibliometric analysis, focusing on its development, essential themes, key contributors, and collaboration patterns. Using statistical text-mining and citation link-based clustering techniques, 690 spatial literacy publications from the Scopus database were analyzed, with data extracted on February 18, 2024. The analysis revealed a steady increase in spatial literacy publications, peaking in 2009, and identified key peaks in cited papers in 1996, 2003 and 2012, with a subsequent decline post-2012. Document profiles primarily consisted of articles (60.14%) and sourced from journals (67.97%), with The Journal of Geography being the most active title. Notably, Newcombe, N.S., emerged as the most productive author, while the work of Bednarz and Kemp received the highest number of citations. ‘The Separability of Working Memory Resources for Spatial Thinking and Language Processing: An Individual Differences Approach’ by Shah and Miyake was identified as the most influential document, garnered a minimum of 100 citations per written document. Additionally, the United States stood out with both the largest number of publications and the highest citation impact in spatial literacy research. This bibliometric analysis provides valuable insight into the spatial literacy research landscape, guiding for future research directions and fostering collaborations in this field.

Overlapping Community Detection Based on Strong Tie Detection and Non-Overlapping Link Clustering

Many clustering algorithms are in favour of node-based methods, but a link between nodes has one single feature, so link-based clustering is sometimes easier than node-based methods. Being dependent on the characteristics of links, a detection algorithm for a non-overlapping link community is put forward in this paper. The method proposed also distinguishes the differences between nodes with a high degree of accuracy and detects communities with a minimal number of overlapping nodes. On the basis of three different datasets, experiments were conducted to compare the proposed algorithm with different non-overlapping and overlapping clustering algorithms, and the results show that our algorithm generates the least number of overlapping nodes and achieves a good community partition.

A Meta-Overview and Bibliometric Analysis of Resilience in Spatial Planning - the Relevance of Place-Based Approaches.

This study offers a literature review and bibliometric analysis aiming to enhance our understanding of the actual contribution of resilience approaches to spatial and territorial development and planning studies. Using citation link-based clustering and statistical text-mining techniques (in terms of prevalence of topics, over time, extraction of relevant terms, keywords frequencies), our study maps scientific domains that include the spatial dimension of resilience thinking. It offers a systematic assessment of modern approaches by connecting profoundly theoretical views to more instrumental and policy-oriented approaches. Firstly, the theoretical background of spatial resilience used in numerous studies in various fields is analysed from the viewpoint of the type of embedded resilience (engineering, ecological, social-ecological, economic, social etc.). Secondly, we review and discuss the significance of three main and consistent research directions in terms of different scales and political/institutional contexts that matter from the viewpoint of spatial and territorial planning. Our findings show that spatial resilience debates are far from being settled, as according to many scientists, resilience measurements are often based on technical-reductionist frameworks that cannot comprehensively reflect the complex systems and issues they address. Our conclusions highlight the necessity of a harmonized framework and integrated perspective on resilience in sustainable territorial planning and development, in both theoretical and empirical contexts.

Using Link-Based Consensus Clustering for Mixed-Type Data Analysis

A mix between numerical and nominal data types commonly presents many modern-age data collections. Examples of these include banking data, sales history and healthcare records, where both continuous attributes like age and nominal ones like blood type are exploited to characterize account details, business transactions or individuals. However, only a few standard clustering techniques and consensus clustering methods are provided to examine such a data thus far. Given this insight, the paper introduces novel extensions of link-based cluster ensemble, and that are accurate for analyzing mixed-type data. They promote diversity within an ensemble through different initializations of the k-prototypes algorithm as base clusterings and then refine the summarized data using a link-based approach. Based on the evaluation metric of NMI (Normalized Mutual Information) that is averaged across different combinations of benchmark datasets and experimental settings, these new models reach the improved level of 0.34, while the best model found in the literature obtains only around the mark of 0.24. Besides, parameter analysis included herein helps to enhance their performance even further, given relations of clustering quality and algorithmic variables specific to the underlying link-based models. Moreover, another significant factor of ensemble size is examined in such a way to justify a tradeoff between complexity and accuracy.

An Effective Link-Based Clustering Algorithm for Detecting Overlapping Protein Complexes in Protein-Protein Interaction Networks

Protein complexes are one most important kind of functional modules for biological processes in cells. In this regard, their detection is vital for understanding the principle of cell organization and function. A variety of clustering algorithms have been developed to detect protein complexes from protein-protein interaction (PPI) networks. However, most of them are based on a certain clustering criterion. Given the fact that proteins should interact with each other rather than act independently, we reason that clustering upon interactions can better characterize protein complexes than upon proteins, thus improving the detection accuracy. To this end, a link-based clustering algorithm has been proposed in this paper to effectively detect overlapping protein complexes. It first measures the similarity between pairwise interactions from the perspectives of network topology and Gene Ontology. The problem of protein complex detection is then formulated as an optimization problem of link-based clustering, which is resolved by the proposed algorithm. This proposed algorithm explores the intrinsic correlation between protein complexes and interactions for detecting functionally significant protein complexes. Experimental results on five independent PPI datasets collected from the species of yeast and human demonstrate that compared with state-of-the-art algorithms, the proposed algorithm has significantly improved the detection accuracy for protein complexes.

EMASS: A Novel Energy, Safety and Mobility Aware-Based Clustering Algorithm for FANETs

The Unmanned Aerial Vehicles (UAVs), organized as a Flying Ad-hoc NETwork (FANET), are used to make effective remote monitoring in diverse applications. Due to their high mobility, their energy consumption is increasingly affected leading to reduced network stability and communication efficiency. The design of node clustering of a FANET needs to consider the number of UAVs in the vicinity (transmission range) in order to ensure an adaptive reliable routing. Novel clustering schemes have been employed to deal with the highly dynamic flying behavior of UAVs and to maintain network stability. In this context, a new clustering algorithm is proposed to address the fast mobility of UAVs and provide safe inter-UAV distance, stable communication and extended network lifetime. The main contributions of this paper are first to extend and improve important metrics used in two well-known algorithms in the literature namely: The Bio-Inspired Clustering Scheme for FANETs (BICSF) and the Energy Aware Link-based Clustering (EALC). Then, exploiting the improved metrics, an Energy and Mobility-aware Stable and Safe Clustering (EMASS) algorithm, built upon new schemes useful for ensuring stability and safety in FANETs, is proposed. The simulation results showed that the EMASS algorithm outperformed the BICSF and the EALC algorithms in terms of better cluster stability, guaranteed safety, higher packet deliverability, improved energy saving and lower delays.

Analisis Cluster Ensemble dalam Pengelompokan Kabupaten/Kota di Provinsi Sulawesi Selatan Berdasarkan Indikator Kinerja Pembangunan Ekonomi Daerah

Abstract. Cluster analysis or group analysis is an analysis method to classify objects of observation into several groups based on their characteristics. Conventional methods namely Hierarchy and Non-Hierarchy are used in the formation of the initial group. However, the results of the grouping formed had mixed results so that the Cluster Ensemble analysis was then used to obtain a good final grouping. The Cluster Ensemble with the Link-Based Cluster Ensemble approach with the Connected Triple Based Similarity (CTS) method resulted in three final group divisions. The evaluation of the grouping performance used, namely Compactness and Davies-Bouldin, stated that the Cluster Ensemble was better than the hierarchical and non-hierarchical methods. The final group that has been formed is described using the average value for each variable in the district / city in South Sulawesi Province. The first group has the characteristics of regional economic development performance that is better than the second and third groups, but for the third group has the lowest characteristics of regional economic development performance from the first and second groups.Keywords : Cluster, Cluster Ensemble, Group Performance Evaluation, Performance, Regional Economic

High-performance link-based cluster ensemble approach for categorical data clustering

In recent years, the clustering ensembles emerged as a problem solver for extracting the data points into clusters in an efficient way. However, still clustering poses a serious issue due to the presence of imperfect information while partitioning the data into clusters. This creates a serious issue in creating an efficient cluster with cluster ensembles. In this paper, we propose a solution to solve the degradation in clustering during data partitioning. The initial clusters are generated using firefly algorithm. A linked cluster ensemble approach uses similarity measurement using multi-viewpoint and weighted triple quality using entropy measurements that ensembles the data points into clusters. This avoids the problem of local optimum and avoids the issues arise from high-dimensional datasets and improve the quality of clustering. Here, the data partitioning is done with bipartite spectral algorithm and similarity measurement. Finally, the artificial neural network is used to generate classified results from the optimized clustered datasets. The experimental results are carried out over UCI repository datasets and the results show that the proposed method attains an effective ensemble clustering with higher clustering accuracy than the conventional ones.

Aggregation of multi-objective fuzzy symmetry-based clustering techniques for improving gene and cancer classification

The current work reports about the application of a cluster ensemble approach in combining results produced by some multiobjective-based clustering techniques. Firstly, some multiobjective-based fuzzy clustering techniques are developed using the search capabilities of differential evolution and particle swarm optimization. Both these clustering techniques utilize a recently developed point symmetry-based distance for allocation of points to different clusters. The appropriate partitioning from a data set is identified by optimizing simultaneously two cluster quality measures, namely Xie–Beni index and FSym-index. First objective function uses Euclidean distance as a similarity measure, and the second objective function uses point symmetry-based distance in its computation. A set of trade-off solutions are produced by each of these clustering techniques on the final Pareto optimal front. Finally, this set of solutions are combined using a link-based cluster ensemble technique. The effectiveness of ensemble techniques is illustrated on partitioning some real-life gene expression and cancer data sets where automatic identification of set of genes or set of cancer tissues is a pressing issue. The potency of the ensemble techniques applied on both the multi-objective DE- and PSO-based clustering approaches is shown in comparison with several state-of-the-art techniques.

Diversity-driven generation of link-based cluster ensemble and application to data classification

Over decades, a large number of research studies have concentrated on improving the accuracy of classification model. This is the case as several types of classifiers prove to be useful in real-life problems, including the prediction of system failure risk and microarray-based cancer diagnosis. Despite this, the accuracy of existing classifiers has been constrained by uninformative variables typically observed in modern data. In addition to feature selection, one may transform the original data to another variation, where only key feature components are included. Unlike conventional transformation-based techniques found in the literature, this paper presents a novel method that makes use of cluster ensembles, specifically the summarized information matrix, as the transformed data for the following classification step. Among different state-of-the-art methods, the link-based cluster ensemble approach (LCE) provides a highly accurate clustering, and thus particularly employed here. This is uniquely coupled with a diversity-driven generation of ensemble, which provides informative and diverse sets of clusterings. The performance of this transformation model is evaluated on published synthetic, standard and gene expression datasets; using C4.5, Naive Bayes, KNN, Neural Network and Random Forest classifiers; in comparison with benchmark techniques. The findings suggest that the new model can improve the classification accuracy of original data and performs better than the other transformation methods investigated in the empirical study.

Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings

Increasing student retention has been a common goal of many academic institutions, especially in the university level. The negative effects of student attrition are evident to students, parents, university and the society as a whole. The first-year students are at the greatest risk of dropping out or not completing their degree on time. With this insight, a number of data mining methods have been developed for early detection of students at risk of dropout, hence the immediate application of assistive measure. As compared to western countries, this subject has attracted only a few studies in Thai university, with educational data mining being limited to the use of conventional classification models. This paper presents the most recent investigation of student dropout at Mae Fah Luang University, Thailand, and the novel reuse of link-based cluster ensemble as a data transformation framework for more accurate prediction. The empirical study on mixed-type data collection related to students’ demographic detail, academic performance and enrollment record, suggests that the proposed approach is usually more effective than several benchmark transformation techniques, across different classifiers.

An Accurate Revelation of the Similarity between Clusters

The structure of the data set playing a vital role in datamining. In concept of datamining information recovery and pattern identification nothing but data clustering. There are multiple clustering algorithms have been commenced to clustering categorical data. Unfortunately these algorithms created an incomplete information. In recent times cluster ensembles have come out as an essential solution to overcome these limitations and to get the excellence results for clustering. A Link-Based similarity measure is proposed to guess unknown values from a link network of clusters and bridges the gap among the task of data clustering and that link examination. It also improves the ability of ensemble methodology for categorical data. A new LinkBased cluster ensemble approach is commenced which is well-organized than the previous model, where a binary cluster association matrix, like matrix is used to create the cluster ensembles. These cluster ensembles have impurity information, to overcome these problem Link-Based similarity algorithm is used to generate an accurate pure clusters.

PathSelClus

Real-world, multiple-typed objects are often interconnected, forming heterogeneous information networks. A major challenge for link-based clustering in such networks is their potential to generate many different results, carrying rather diverse semantic meanings. In order to generate desired clustering, we propose to use meta-path , a path that connects object types via a sequence of relations, to control clustering with distinct semantics. Nevertheless, it is easier for a user to provide a few examples (seeds) than a weighted combination of sophisticated meta-paths to specify her clustering preference. Thus, we propose to integrate meta-path selection with user-guided clustering to cluster objects in networks, where a user first provides a small set of object seeds for each cluster as guidance. Then the system learns the weight for each meta-path that is consistent with the clustering result implied by the guidance, and generates clusters under the learned weights of meta-paths. A probabilistic approach is proposed to solve the problem, and an effective and efficient iterative algorithm, PathSelClus , is proposed to learn the model, where the clustering quality and the meta-path weights mutually enhance each other. Our experiments with several clustering tasks in two real networks and one synthetic network demonstrate the power of the algorithm in comparison with the baselines.

A Bootstrap Aggregating Technique on Link-Based Cluster Ensemble Approach for Categorical Data Clustering

Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data point relations, with many entries being left unknown. The paper presents an analysis that suggests this problem degrades the quality of the clustering result, and it presents a BSA (Bootstrap Aggregation) is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy along with a new link-based approach, which improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble. In particular, an efficient BSA and link-based algorithm is proposed for the underlying similarity assessment. Afterward, to obtain the final clustering result, a graph partitioning technique is applied to a weighted bipartite graph that is formulated from the refined matrix. Experimental results on multiple real data sets suggest that the proposed link-based method almost always outperforms both conventional clustering algorithms for categorical data and well-known cluster ensemble techniques.

Application of the Link-based Clustering in The Financial Supervision

Application of the Link-based Clustering in The Financial Supervision

On the Use of a Cluster Ensemble Cloud Classification Technique in Satellite Precipitation Estimation

In this paper, the link-based cluster ensemble (LCE) method is utilized to improve cloud classification and satellite precipitation estimation. High resolution Satellite Precipitation Estimation (SPE) is based on the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. This modified SPE with the incorporation of LCE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering cloud patches using LCE; and 4) dynamic application of brightness temperature (Tb) and rain-rate relationships, derived using satellite observations. In order to cluster the cloud patches, the LCE method combines multiple data partitions from different clustering methods. The results show that using the cluster ensemble increases the performance of rainfall estimates compared to the SPE algorithm using a Self Organizing Map (SOM) neural network. The false alarm ratio (FAR), probabilities of detection (POD), equitable threat score (ETS), and bias are used as quantitative measures to assess the performance of the algorithm. It is shown that both the ETS and bias provide improvement in the summer and winter seasons. Almost 5% ETS improvement is obtained at some threshold values for the winter season using the cluster ensemble.

LCE: a link-based cluster ensemble method for improved gene expression data analysis

It is far from trivial to select the most effective clustering method and its parameterization, for a particular set of gene expression data, because there are a very large number of possibilities. Although many researchers still prefer to use hierarchical clustering in one form or another, this is often sub-optimal. Cluster ensemble research solves this problem by automatically combining multiple data partitions from different clusterings to improve both the robustness and quality of the clustering result. However, many existing ensemble techniques use an association matrix to summarize sample-cluster co-occurrence statistics, and relations within an ensemble are encapsulated only at coarse level, while those existing among clusters are completely neglected. Discovering these missing associations may greatly extend the capability of the ensemble methodology for microarray data clustering. The link-based cluster ensemble (LCE) method, presented here, implements these ideas and demonstrates outstanding performance. Experiment results on real gene expression and synthetic datasets indicate that LCE: (i) usually outperforms the existing cluster ensemble algorithms in individual tests and, overall, is clearly class-leading; (ii) generates excellent, robust performance across different types of data, especially with the presence of noise and imbalanced data clusters; (iii) provides a high-level data matrix that is applicable to many numerical clustering techniques; and (iv) is computationally efficient for large datasets and gene clustering. Online supplementary and implementation are available at: http://users.aber.ac.uk/nii07/bioinformatics2010. Supplementary data are available at Bioinformatics online.

LinkCluE: AMATLABPackage for Link-Based Cluster Ensembles

Cluster ensembles have emerged as a powerful meta-learning paradigm that provides improved accuracy and robustness by aggregating several input data clusterings. In particular, link-based similarity methods have recently been introduced with superior performance to the conventional co-association approach. This paper presents a <b>MATLAB</b> package, <b>LinkCluE</b>, that implements the link-based cluster ensemble framework. A variety of functional methods for evaluating clustering results, based on both internal and external criteria, are also provided. Additionally, the underlying algorithms together with the sample uses of the package with interesting real and synthetic datasets are demonstrated herein.

A Framework for Summarization of Multi-topic Web Sites

Web site summarization, which identifies the essential content covered in a given Web site, plays an important role in Web information management. However, straightforward summarization of an entire Web site, which is large and with diverse content, may lead to a summary heavily biased to a subset of main topics covered in the target Web site. In this thesis, we propose a two-stage framework for effective summarization of multi-topic Web sites. The first stage identifies the main topics covered in a Web site and the second stage summarizes each topic separately. In order to identify the different topics covered in a Web site, we perform both text- and link-based clustering. In text-based clustering, we investigate the impact of document representation and feature selection on the clustering quality. In link-based clustering, we study co-citation and bibliographic coupling. We demonstrate that text-based clustering based on the selection of features with high variance over Web pages is reliable and that outgoing links can be used to improve the clustering quality if a rich set of cross links is available. Each individual cluster computed above is summarized using an extraction-based summarization system, which extracts key phrases and key sentences from source documents to generate a summary. The performance of such an extraction-based Web site summarization system depends on its underlying key phrase extraction method. Hence, we conduct a user study to investigate five alternative key phrase extraction methods. Results show that the best method combines linguistic constraints with frequency over the corpus adjusted to take into account nesting of terms. Another important component in an extraction based summarization system is the key sentence extraction. To this end, we design and develop a classification approach in the cluster summarization stage. The classifier uses statistical and linguistic features to determine the topical significance of each sentence. Finally, we evaluate the proposed system via a user study. We demonstrate that the proposed clustering summarization approach significantly outperforms the single-topic summarization approach for any given Web site summarization task.

Document clustering of scientific texts using citation contexts

Document clustering has many important applications in the area of data mining and information retrieval. Many existing document clustering techniques use the "bag-of-words" model to represent the content of a document. However, this representation is only effective for grouping related documents when these documents share a large proportion of lexically equivalent terms. In other words, instances of synonymy between related documents are ignored, which can reduce the effectiveness of applications using a standard full-text document representation. To address this problem, we present a new approach for clustering scientific documents, based on the utilization of citation contexts. A citation context is essentially the text surrounding the reference markers used to refer to other scientific works. We hypothesize that citation contexts will provide relevant synonymous and related vocabulary which will help increase the effectiveness of the bag-of-words representation. In this paper, we investigate the power of these citation-specific word features, and compare them with the original document's textual representation in a document clustering task on two collections of labeled scientific journal papers from two distinct domains: High Energy Physics and Genomics. We also compare these text-based clustering techniques with a link-based clustering algorithm which determines the similarity between documents based on the number of co-citations, that is in-links represented by citing documents and out-links represented by cited documents. Our experimental results indicate that the use of citation contexts, when combined with the vocabulary in the full-text of the document, is a promising alternative means of capturing critical topics covered by journal articles. More specifically, this document representation strategy when used by the clustering algorithm investigated in this paper, outperforms both the full-text clustering approach and the link-based clustering technique on both scientific journal datasets.