A Divide-Merge Clustering Algorithm Based on Dimension Importance and Inter-Subcluster Tightness

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PDF

Each clustering algorithm usually optimizes a qualification metric during its learning process. The qualification metric in traditional clustering algorithms considers all the features of under-consideration dataset equally; it means each feature participates in the clustering process equivalently. Considering that some features have more information than the others in a dataset (due to their lower information or their higher variances, etc.), we proposes a fuzzy weighted clustering algorithm. We name this new clustering algorithm, Fuzzy Weighted Locally Adaptive Clustering (FWLAC) algorithm. The proposed FWLAC algorithm is capable of handling imbalanced clustering. However, FWLAC algorithm suffers from its sensitivity to the two parameters that should be tuned manually. The performance of FWLAC algorithm is affected by well-tuning of its parameters. So the paper proposes two solutions to well-tuning of its two parameters. In the first solution, we propose a simple clustering ensemble framework to show the sensitivity of the WLAC algorithm to its manual well-tuning. Although it is not a try-and-error procedure, it is like a grid search, where we use different pairs of values for both parameters h 1 and h 2. Per each pair of values for parameters h 1 and h 2, the algorithm produces a partitioning. So after the grid search, we obtain a large number of partitionings. We break any of the partitionings into its clusters, and then they form an ensemble of clusters. Finally the consensus partitioning is extracted from them by a consensus function. The algorithm is not data dependent at all. For all datasets, we use a similar grid search and a similar set of values for the parameters h 1 and h 2. In this way we have proposed an alternative solution to parameter selection. We use a selection phase to select/remove some clusters from our ensemble of clusters to obtain an elite ensemble of clusters. To do this, a stability measure, normalized mutual information (NMI), was used to validate a cluster. The paper shows the effectiveness of the proposed clustering frameworks both theoretically and experimentally.

An image segmentation method based on a modified local-information weighted intuitionistic Fuzzy C-means clustering and Gold-panning Algorithm

Modified FDP cluster algorithm and its application in protein conformation clustering analysis

Inspiration from nature has driven many creative solutions to challenging real life problems. Many optimization methods, in particular clustering algorithms, have been inspired by such natural phenomena as neural systems and networks, natural evolution, the immune system, and lately swarms and colonies. In this paper, we make a brief survey of swarm intelligence clustering algorithms and focus on the flocks of agents-based clustering and data visualization algorithm, (FClust). A few limitations of FClust are then discussed with proposed improvements.We thus propose the FClust-annealing algorithm that decreases the number of iterations needed to converge and improves the quality of resulting clusters. We also propose a (K-means+ FClust) hybrid algorithm which decreases the complexity of FClust from quadratic to linear, with further improvements in the cluster quality. Experiments on both artificial and real data illustrate the workings of FClust and the advantages of our proposed variants.

Recently, a variety of data acquisition methods leads to the description of the same thing from different angles, which induces a large number of multi-view data. Clustering multi-view data in large-scale data analysis is of great significance and very challenging. In this paper, we present a new robust clustering algorithm, the Multi-view Fuzzy K-means clustering (MFKC) algorithm. In MFKC, a fuzzy membership method is used and optimized to describe the probability of a sample belonging to certain clusters. Compared with the hard partition based multi-view clustering algorithms, MFKC can effectively describe the fuzzy relationship between multi-view data and all clusters, and has better interpretability for the clustering results. In addition, MFKC also sets different weights for different views. By optimizing the multi-view clustering objective function, we obtain the optimal weigh of each view to reflect the importance of the view. The feasibility and effectiveness of the proposed algorithm is validated through various experimental comparisons.

Clustering of gene expression data is a standard exploratory technique used to identify closely related genes. Many other sources of data are also likely to be of great assistance in the analysis of gene expression data. This data provides a mean to begin elucidating the large-scale modular organization of the cell. The authors consider the challenging task of developing exploratory analytical techniques to deal with multiple complete and incomplete information sources. The Multi-Source Clustering (MSC) algorithm developed performs clustering with multiple, but complete, sources of data. To deal with incomplete data sources, the authors adopted the MPCK-means clustering algorithms to perform exploratory analysis on one complete source and other potentially incomplete sources provided in the form of constraints. This paper presents a new clustering algorithm MSC to perform exploratory analysis using two or more diverse but complete data sources, studies the effectiveness of constraints sets and robustness of the constrained clustering algorithm using multiple sources of incomplete biological data, and incorporates such incomplete data into constrained clustering algorithm in form of constraints sets.

The first synchronization clustering (SynC) algorithm based on an extensive Kuromato model was presented in 2010. In 2017, an effective synchronization clustering (ESynC) algorithm, inspired by SynC algorithm and a linear version of Vicsek model, was proposed. When facing complex data distributions, ESynC algorithm may regard an irregular and whole cluster as some micro-clusters. To conquer this shortcoming, a combined clustering algorithm based on ESynC algorithm and a merging judgement process of micro-clusters (CESynC) is presented. CESynC algorithm first uses ESynC algorithm to detect clusters or micro-clusters, then merges those conjoint micro-clusters by using a merging judgement process. For some datasets that ESynC and SynC cannot detect correct clusters, CESynC can capture natural clusters. From the simulation experiments, we observe that CESynC can get better (or the same) clustering results than (or as) that of ESynC in many cases. We also observe that the clustering results of CESynC and ESynC are often better than that of SynC. Therefore, we can say CESynC can often obtain better clustering quality than ESynC and SynC in some kinds of datasets. Further comparison experiments with some classical clustering algorithms demonstrate the clustering effect of CESynC.

Compressional and shear travel time logs (DTC and DTS) acquired using sonic logging tools are crucial for subsurface geomechanical characterization. In this study, 13 ‘easy-to-acquire’ conventional logs were processed using 6 shallow learning models, namely ordinary least squares (OLS), partial least squares (PLS), elastic net (EN), LASSO, multivariate adaptive regression splines (MARS), and artificial neural network (ANN), to successfully synthesize DTC and DTS logs. Among the 6 models, ANN outperforms other models with R2 of 0.87 and 0.85 for the syntheses of DTC and DTS logs, respectively. The 6 shallow learning models are trained and tested with 8481 data points acquired from a 4240-feet depth interval of a shale reservoir in Well 1, and the trained models are deployed in Well 2 for purposes of blind testing against 2920 data points from 1460-feet depth interval. Following that, 5 clustering algorithms are applied on the 13 ‘easy-to-acquire’ logs to identify clusters and compare them with the prediction performance of the shallow learning models used for log synthesis. Dimensionality reduction algorithm is used to visualize the characteristics of the clustering algorithm. Hierarchical clustering, DBSCAN, and self-organizing map (SOM) algorithms are sensitive to outliers and did not effectively differentiate the input data into consistent clusters. Gaussian mixture model can well differentiate the various formations, but the clusters do not have a strong correlation with the prediction performance of the log-synthesis models. Clusters identified using K-means method have a strong correlation with the prediction performance of the shallow learning models. By combining the predictive shallow learning models for log synthesis with the K-means clustering algorithm, we propose a reliable workflow that can synthesize the DTC and DTS logs, as well as generate a reliability indicator for the predicted logs to help an user better understand the performance of the shallow learning models during deployment.

In recent past, the features selection plays a significant role in machine learning algorithms to improve their performance in terms of increasing the classification accuracy and reducing the classifier building time. Therefore, it is essential to evaluate the performance of the features selection method that is proposed. Moreover, the clustering algorithm also be used some cases in the feature selection process for removing the redundant features from the high-dimensional space therefore identifying the suitable clustering algorithm also be important. Hence, this paper presents the performance metrics for feature selection, clustering and classification algorithms.

Wireless sensor networks have enticed lot of spotlight from researchers all around globe, owing to its wide applications in industrial, military and agricultural fields. Energy conservation and node deployment strategies play a paramount role for effective execution of Wireless Sensor Networks. Clustering of nodes in the wireless sensor networks is an approach commenced to achieve energy efficiency in the network. Clustering algorithm, if not executed properly can reduce life of the network. In this paper, a Two -Step Uniform Clustering (TSUC) algorithm has been proposed with the aim to provide connectivity to the nodes in every part of the network. This algorithm increases networks lifetime and throughput by re-clustering isolated nodes rather than providing them connectivity by already connected node. Results obtained after simulation showed that proposed TSUC algorithm performed better than the other existing clustering algorithm.

The clustering algorithm of Protein-Protein Interaction(PPI) networks is an important method to fully understand the organizations and functions of molecules and identify the functional modules of protein.There are lots of traditional clustering algorithms which do not perform well in clustering PPI networks.Recently functional flow simulation algorithm is a novel clustering algorithm.However,it does not take the effect of distance into account and the merging threshold is set manually which is subjective.This paper proposes a novel information flow clustering model and algorithm based on the mechanism of Artificial Bee Colony(ABC) optimization.This method firstly sorts the network comprehensive feature value of nodes to initialize the cluster centers during the procedure of data pre-processing.The nectar source of ABC algorithm is corresponding to cluster center,the income level of nectar stands for the similarity between modules.Afterwards all the adjacent nodes of employed bee node are sorted in the descending order according to the network comprehensive feature value of nodes,which are regarded as the searching neighborhood of scouts.In the end,the algorithm adopts precision,recall and other criteria to evaluate the cluster effect in an objective way.In addition,some significant parameters of the algorithm is simulated,compared and analyzed.The experiment results show that the new algorithm not only overcomes the shortcomings of original algorithm,but also the harmonic mean value of precision and recall gets greatly improved,which can effectively identify the functional modules of protein.

Big data is technology that has the ability to manage very large amounts of data, in very fast time to allow real-time analysis and reactions. Several clustering methods which are used to group data are Fuzzy C-Means (FCM) and K-Means Clustering. K-Means Clustering algorithm is a method of partitioning existing data into two or more group. This research goal was to compare the performance of K-Means and Fuzzy C-Means algorithms in clustering data using big data technology. In this research, Hadoop and Hive were chosen the big data technology. The knowledge of Shia history on student and lecturer of Syarif Hidayatullah State Islamic University Jakarta were the data which used in this research, The testing was done by constructing application K-Means Fuzzy C-Means using Java language, Hadoop and Hive and then test the performance of K-Means and Fuzzy C-Means algorithms in data clustering. It compares both algorithms in terms of accuracy, execution time, and time complexity of the algorithms. In the application K-Means Fuzzy C-Means, evaluation were performed with data filter and the average accuracy difference result of K-Means and Fuzzy C-Means is 8.03% with the better accuracy owned by K-Means. The average execution time difference is 718.58 ms, which K-Means was faster than is Fuzzy C-Means. The time complexities of both algorithms have the same value O(n2) and the Big O equation resulted in an average difference of 93,568 with the smallest value on K-Means. Thus, K-Means algorithm is better than the Fuzzy C-Means in terms of accuracy, execution time, and the time complexity.

Advances made to the traditional clustering algorithms solves the various problems such as curse of dimensionality and sparsity of data for multiple attributes. The traditional H-K clustering algorithm can solve the randomness and apriority of the initial centers of K-means clustering algorithm. But when we apply it to high dimensional data it causes the dimensional disaster problem due to high computational complexity. All the advanced clustering algorithms like subspace and ensemble clustering algorithms improve the performance for clustering high dimension dataset from different aspects in different extent. Still these algorithms will improve the performance form a single perspective. The objective of the proposed model is to improve the performance of traditional H-K clustering and overcome the limitations such as high computational complexity and poor accuracy for high dimensional data by combining the three different approaches of clustering algorithm as subspace clustering algorithm and ensemble clustering algorithm with H-K clustering algorithm.

In this study, the concepts of competitive agglomeration clustering algorithm is incorporated into fuzzy c-means (FCM) clustering algorithm for symbolic interval-values data. In the proposed approach, called as IFCMwUNC clustering algorithm, the problems of the unknown clusters number and the initialization of prototypes in the FCM clustering algorithm for symbolic interval-values data are overcome and discussed. Due to the competitive agglomeration clustering algorithm possess the advantages of the hierarchical clustering algorithm and the partitional clustering algorithm, IFCMwUNC clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments results show the merits and usefulness of IFCMwUNC clustering algorithm for the symbolic interval-values data.