Multi-stage Clustering Algorithm Research Articles

PENERAPAN MULTI-CLUSTERING DALAM PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA BARAT BERDASARKAN INDEKS DESA MEMBANGUN

Cluster analysis is a statistical learning technique that aims to uncover hidden patterns in data by grouping it based on known explanatory variables. In multi-clustering algorithms, similar data in different sub-dimensions of categories are initially grouped separately and then re-categorized based on the obtained clusters, resulting in a more exploratory grouping. This research aims to conduct exploratory analysis of regencies/cities in West Java based on the Village Development Index (Indeks Desa Membangun/IDM), which consists of three sub-dimensions: Social Resilience Index, Economic Index, and Environmental Index. It also aims to observe how the regencies/cities in West Java are grouped based on these indices using a multi-clustering algorithm with KMeans for each sub-dimension. From the exploration and analysis results, regencies/cities are clustered based on the three sub-dimensions. Additionally, recommendations are obtained suggesting that the equal distribution of educational facilities, addressing crime rates, improving economic infrastructure, and enhancing environmental quality should be priorities for the government of West Java province

A Novel Weighted Data-Driven Robust Optimization Approach for Creating Adjustable Uncertainty Sets

Robust optimization provides a progressive and efficient approach to dealing with uncertainty which applies to various optimization problems. However, most existing robust optimization approaches generate a unique uncertainty set for each level of conservatism, disregarding supplementary information such as predicted values. This paper proposes a new weighted data-driven robust optimization approach for creating adjustable uncertainty sets. The proposed approach enables the adjustment of the boundary reduction rate of uncertainty sets, effectively pulling uncertainty sets towards areas of higher density or towards the predicted points. Additionally, a multi-stage clustering algorithm is proposed to fully cover the areas around the predicted point. A regularization parameter search algorithm is also developed to tune the conservatism degree. The numerical results indicate that by weighting historical data, adjustable uncertainty sets with the same fraction of data coverage can be created that ensures the feasibility of the model and reduces the extra conservatism.

A novel high-dimensional trajectories construction network based on multi-clustering algorithm

A multiple clustering algorithm based on high-dimensional automatic identification system (AIS) data is proposed to extract the important waypoints in the ship’s navigation trajectory based on selected AIS attribute features and construct a route network using the waypoints. The algorithm improves the accuracy of route network planning by using the latitude and longitude of the historical voyage trajectory and the heading to the ground. Unlike the navigation clustering method that only uses ship latitude and longitude coordinates, the algorithm first calculates the major waypoints using Clustering in QUEst (CLIQUE) and Balance Iterative Reducing and Clustering Using Hierarchies (BIRCH) algorithms, and then builds the route network using network construction. Under the common PC specification (i5 processor), this algorithm forms 440 major waypoints from 220,133 AIS data and constructs a route network with directional features in 5 min, which is faster in computing speed and more suitable for complex ship trajectory differentiation and can extend the application boundary of ship route planning.

A Multi-Clustering Algorithm to Solve Driving Cycle Prediction Problems Based on Unbalanced Data Sets: A Chinese Case Study.

Vehicle evaluation parameters, which are increasingly of concern for governments and consumers, quantify performance indicators, such as vehicle performance, emissions, and driving experience to help guide consumers in purchasing cars. While past approaches for driving cycle prediction have been proven effective and used in many countries, these algorithms are difficult to use in China with its complex traffic environment and increasingly high frequency of traffic jams. Meanwhile, we found that the vehicle dataset used by the driving cycle prediction problem is usually unbalanced in real cases, which means that there are more medium and high speed samples and very few samples at low and ultra-high speeds. If the ordinary clustering algorithm is directly applied to the unbalanced data, it will have a huge impact on the performance to build driving cycle maps, and the parameters of the map will deviate considerable from actual ones. In order to address these issues, this paper propose a novel driving cycle map algorithm framework based on an ensemble learning method named multi-clustering algorithm, to improve the performance of traditional clustering algorithms on unbalanced data sets. It is noteworthy that our model framework can be easily extended to other complicated structure areas due to its flexible modular design and parameter configuration. Finally, we tested our method based on actual traffic data generated in Fujian Province in China. The results prove the multi-clustering algorithm has excellent performance on our dataset.

Adaptive MCFL: An adaptive multi-clustering algorithm using fuzzy logic in wireless sensor network

In the past, numerous algorithms were tasked with clustering nodes in wireless sensors networks. Each of these algorithms has its own advantages and disadvantages. The common denominator of all these approaches is the constancy of the algorithm in all the rounds of network lifetime that causes the selection of cluster heads in each round. Failing to select the best nodes as cluster heads leads to holding elections in each round. By comparing the chance of each node to be selected as a cluster head using a random number, the majority of these clustering approaches, both fuzzy and non-fuzzy, destroy the chance of selecting the most eligible node as cluster head. As a result, all these approaches require the selection of cluster heads in each round. Selecting cluster heads in each round increases the amount of received and sent messages such that in networks with large number of nodes, it causes some problems such as energy reduction, collision increase, and network traffic. However, by selecting the most eligible nodes as cluster heads and trusting them for at least a few rounds, the amount of sent and received messages is reduced. In this article, an adaptive multiclustering algorithm using fuzzy logic in wireless sensor network (Adaptive MCFL) is presented. In addition to clustering nodes in different rounds using different clustering algorithms, the proposed algorithm avoids selecting new cluster heads by trusting previous cluster heads leading to a reduction in the number of messages and saving energy. The proposed approach is compared with other approaches in three different scenarios using indices such as remaining energy, the number of dead nodes, first node dies (FND), half of nodes die (HND), and last node dies (LND). Results reveal that Adaptive MCFL has as advantage over other approaches.

MCFL: an energy efficient multi-clustering algorithm using fuzzy logic in wireless sensor network

In this study, a multi-clustering algorithm based on fuzzy logic (MCFL) with an entirely different approach is presented to carry out node clustering in wsn. This approach minimizes energy dissipation and, consequently, prolongs network lifetime. In the past, numerous algorithms were tasked with clustering nodes in wireless sensors networks. The common denominator of all these approaches is the constancy of the algorithm in all the rounds of network lifetime that causes the selection of cluster heads in each round. Selecting cluster heads in each round indicates that throughout the process the most eligible nodes are not selected. By comparing the chance of each node to be selected as a cluster head using a random number, the majority of these clustering approaches, both fuzzy and non-fuzzy, destroy the chance of selecting the most eligible node as cluster head. As a result, all these approaches require the selection of cluster heads in each round. Performing selections in each round increases the rate of sent and received messages. By increasing the number of messages, the total number of sent messages in the network increases too. Therefore, in a network with a high number of nodes, any increase in the number of packets will augment network traffic and increase the collision probability. On the other hand, since nodes lose a certain amount of energy for each sent message, by increasing the number of messages, nodes’ energy will correspondingly decrease which results in their premature death. However, by selecting the most eligible nodes as cluster heads and trusting them for at least a few rounds, the amount of sent and received messages is reduced. In this article, In addition to clustering nodes in different rounds using different clustering algorithms, MCFL avoids selecting new cluster heads by trusting previous cluster heads leading to a reduction in the number of messages and saving energy. MCFL is compared with other approaches in three different scenarios using indices such as total remaining energy, the number of dead nodes, first node dies, half of nodes die, and last node dies. Results reveal that MCFL has as advantage over other approaches.

XMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.

Improved analytical technologies and data extraction algorithms enable detection of >10 000 reproducible signals by liquid chromatography-high-resolution mass spectrometry, creating a bottleneck in chemical identification. In principle, measurement of more than one million chemicals would be possible if algorithms were available to facilitate utilization of the raw mass spectrometry data, especially low-abundance metabolites. Here we describe an automated computational framework to annotate ions for possible chemical identity using a multistage clustering algorithm in which metabolic pathway associations are used along with intensity profiles, retention time characteristics, mass defect, and isotope/adduct patterns. The algorithm uses high-resolution mass spectrometry data for a series of samples with common properties and publicly available chemical, metabolic, and environmental databases to assign confidence levels to annotation results. Evaluation results show that the algorithm achieves an F1-measure of 0.8 for a data set with known targets and is more robust than previously reported results for cases when database size is much greater than the actual number of metabolites. MS/MS evaluation of a set of randomly selected 210 metabolites annotated using xMSannotator in an untargeted metabolomics human data set shows that 80% of features with high or medium confidence scores have ion dissociation patterns consistent with the xMSannotator annotation. The algorithm has been incorporated into an R package, xMSannotator, which includes utilities for querying local or online databases such as ChemSpider, KEGG, HMDB, T3DB, and LipidMaps.

An Enhanced Equilin Multi-Clustering Algorithm for Constructing Numeric Clusters

An Enhanced Equilin Multi-Clustering Algorithm for Constructing Numeric Clusters

Energy-efficient and Multi-stage Clustering Algorithm in Wireless Sensor Networks Using Cellular Learning Automata

One of the main challenges in wireless sensor networks is the energy constraints of sensor nodes which must be considered precisely when designing algorithms for such networks. Clustering is known as one of the approaches which can be used for addressing this challenge. In this paper, an efficient method for clustering wireless sensor networks by means of cellular learning automata has been presented (LaClustering). Proposed method selects cluster head (CHs) through several stages; each considers one parameter affecting the overall performance of the clustering. Parameters considered in different stages of the proposed algorithm are energy levels of the sensor nodes, number of neighbors of each node, network connectivity, and formation of balanced clusters. To evaluate the performance of the proposed method, several experiments have been conducted using the J-sim simulator and the proposed method has been compared with some of the best clustering algorithms reported in literature. The simulation results have shown that the proposed algorithm can provide clustering infrastructure with higher overall quality than the existing algorithms, especially in balancing the number of sensor nodes in different clusters and selecting CHs with higher energy levels.

Improve the Performance of Clustering Using Combination of Multiple Clustering Algorithms

The ever-increasing availability of textual documents has lead to a growing challenge for information systems to effectively manage and retrieve the information comprised in large collections of texts according to the user�s information needs. There is no clustering method that can adequately handle all sorts of cluster structures and properties (e.g. shape, size, overlapping, and density). Combining multiple clustering methods is an approach to overcome the deficiency of single algorithms and further enhance their performances. A disadvantage of the cluster ensemble is the highly computational load of combing the clustering results especially for large and high dimensional datasets. In this paper we propose a multiclustering algorithm , it is a combination of Cooperative Hard-Fuzzy Clustering model based on intermediate cooperation between the hard k-means (KM) and fuzzy c-means (FCM) to produce better intermediate clusters and ant colony algorithm. This proposed method gives better result than individual clusters.

Video personalization in heterogeneous and resource-constrained environments

Access to multimedia data and multimedia services is becoming increasingly common in networked mobile environments. In such environments, both the mobile client devices and multimedia servers are typically resource constrained. Moreover, the mobile client population is often heterogeneous in terms of the clients' preferences with regard to multimedia content, the clients' quality of service requirements and system-level resource constraints. In order to provide a resource-constrained mobile client with its desired video content, it is often necessary to personalize the requested multimedia content in a manner that satisfies simultaneously the various client-specified content preferences and the system-level resource constraints. Also, in order to simultaneously reduce the client-experienced latency, provide optimal quality of service to the clients and ensure efficient utilization of server and network resources, it is necessary to perform client request aggregation on the server end. To this end, a video personalization strategy is proposed to provide mobile, resource-constrained clients with personalized video content that is most relevant to the clients' requests while satisfying simultaneously multiple client-side system-level resource constraints. A client request aggregation strategy is also proposed to cluster client requests with similar video content preferences and similar client-side resource constraints such that the number of requests the server needs to process and service, and the client-experienced latency are both reduced simultaneously. The primary contributions of the paper are: (1) the formulation and implementation of a Multiple-choice Multi-dimensional Knapsack Problem (MMKP)-based video personalization strategy; and (2) the design and implementation of a client request aggregation strategy based on a multi-stage clustering algorithm. Experimental results comparing the proposed MMKP-based video personalization strategy to existing 0/1 Knapsack Problem (0/1KP)-based and the Fractional Knapsack Problem (FKP)-based video personalization strategies are presented. It is observed that: (1) the proposed MMKP-based personalization strategy includes more relevant video content in response to the client's request compared to the existing 0/1KP-based and FKP-based personalization strategies; and (2) in contrast to the 0/1KP-based and FKP-based personalization strategies which can satisfy only a single client-side resource constraint at a time, the proposed MMKP-based personalization strategy is capable of satisfying multiple client-side resource constraints simultaneously. Experimental results comparing the client-experienced latency with and without the proposed server-side client request aggregation strategy are also presented. It is shown that the proposed client request aggregation strategy reduces the mean client-experienced latency without significant reduction in the average relevance of the delivered video content and without significant deviation in the client-side resources actually consumed by the delivered video content from the client-specified resource constraints.

Architectural partitioning for system level synthesis of integrated circuits

APARTY is an architectural partitioning tool that uses a novel multistage clustering algorithm to extract the high level structure of an IC design by concentrating on area and interconnect considerations. Performance is addressed implicitly. APARTY works within the framework of the system architect's workbench and can pass system-level structural information along to register-transfer level (RTL) tools to guide the completion of a data-path design. The multistage clustering algorithm and how it is used by APARTY to choose partitions are described. The system architect's workbench and how architectural partitioning can be used to guide synthesis are also described. Results of using APARTY in the design process show improved register-transfer designs. In particular, the number of global routing wires is generally reduced by over 50% by following the partitioning scheme suggested by APARTY.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>