Incident Clustering in the Warehouse Workspaces by Using Text Mining

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.

In this study, a K-means clustering (KMC) method was used to identify the paths of the Northeast China (NEC) Cold Vortex (NCCV). The NCCV was divided into four types according to the identified active paths: (1) Eastward movement type (EM); (2) Southeastward long-distance movement type (SLM); (3) Eastward short-distance movement type (ESM); and (4) Southward short-distance movement type (SSM). The characteristics of the four types of the NCCV, along with their impacts on the precipitation during early summer in NEC, were studied. The results showed that the KMC method can effectively divide the NCCV events into four different types. The maintaining days of these four types of the NCCV were found to have obvious interannual and interdecadal variation features. For example, the maintaining days of the EM and ESM types were mainly characterized by interannual variability, while the SLM and SSM types have the obvious 10–13a interdecadal variation along with interannual variability. In terms of the spatial distributions and impacts on precipitation, the EM type was found to appear in the majority of the areas located in NEC, the SLM type mainly occurred in the northwestern region of NEC and the highest rain center was located in the south-central portion, while the ESM type and SSM type were observed precipitation only appear in a small portion of the northeastern region. In addition, it is also observed the distribution of the sea-surface temperature (SST) anomalies had close relationship with the formation of these four types of the NCCV. The tripole distributions of the SST anomalies in the Atlantic Ocean corresponded to the EM type of the NCCV, the positive anomalies of SST in the eastern equatorial Pacific Ocean and negative anomalies in the western equatorial Pacific corresponded to the SLM type, the positive SSTs in the Northwest Pacific correspond to the ESM type, while negative anomalies SST in the western equatorial Pacific Ocean corresponded to the SSM type of the NCCV.

Hyperspectral image analysis of Raman maps of plant cell walls for blind spectra characterization by nonnegative matrix factorization algorithm

Application of hierarchical clustering, singularity mapping, and Kohonen neural network to identify Ag-Au-Pb-Zn polymetallic mineralization associated geochemical anomaly in Pangxidong district

Supplementary Material for the manuscript "Drivers of carbon dioxide emissions: An empirical investigation using hierarchical and non- hierarchical clustering methods"

The study aimed to evaluate the accuracy of determining sapodilla fruit maturity using the K-Means Clustering algorithm, a method that partitions data into clusters of similar characteristics; by applying K-Means Clustering on data samples obtained from Kaggle, and using Matlab for pixel value calculation, the algorithm effectively classified 150 sapodilla fruit samples into two clusters—75 mature and 75 raw—with an accuracy of 92.2% for mature sapodilla and 94.5% for raw sapodilla, demonstrating that K-Means Clustering, a straightforward and user-friendly algorithm, is highly effective in distinguishing sapodilla fruit maturity levels.

Clustering is a group of unsupervised statistical techniques commonly used in many disciplines. Considering their applications to fish abundance data, many technical details need to be considered to ensure reasonable interpretation. However, the reliability and stability of the clustering methods have rarely been studied in the contexts of fisheries. This study presents an intensive evaluation of three common clustering methods, including hierarchical clustering (HC), K-means (KM), and expectation-maximization (EM) methods, based on fish community surveys in the coastal waters of Shandong, China. We evaluated the performances of these three methods considering different numbers of clusters, data size, and data transformation approaches, focusing on the consistency validation using the index of average proportion of non-overlap (APN). The results indicate that the three methods tend to be inconsistent in the optimal number of clusters. EM showed relatively better performances to avoid unbalanced classification, whereas HC and KM provided more stable clustering results. Data transformation including scaling, square-root, and log-transformation had substantial influences on the clustering results, especially for KM. Moreover, transformation also influenced clustering stability, wherein scaling tended to provide a stable solution at the same number of clusters. The APN values indicated improved stability with increasing data size, and the effect leveled off over 70 samples in general and most quickly in EM. We conclude that the best clustering method can be chosen depending on the aim of the study and the number of clusters. In general, KM is relatively robust in our tests. We also provide recommendations for future application of clustering analyses. This study is helpful to ensure the credibility of the application and interpretation of clustering methods.

The distribution of Non-Cash Food Assistance (BPNT) in Rajadesa Village often encounters challenges in accurately determining recipient eligibility. The selection process lacks objectivity due to limited data and suboptimal verification, leading to uneven distribution. To address this issue, the study employs the K-Means Clustering algorithm to enhance the efficiency of BPNT recipient selection. The assessment is based on attributes such as ID number (NIK), name, address, occupation, and the amount of assistance received.The methodology adopts the Knowledge Discovery in Databases (KDD) approach, involving stages such as data selection, preprocessing, transformation, data mining, and evaluation. Data processing is carried out using RapidMiner version 10.5. The clustering results are evaluated using the Davies Bouldin Index (DBI), yielding the best model with three clusters and a DBI value of 0.346. This approach successfully identifies recipient groups more accurately, enabling a more targeted distribution of food assistance. Consequently, the study provides significant contributions to ensuring the eligibility of BPNT recipients in Rajadesa Village through the application of K-Means Clustering-based analytical technology.

Indonesia is the country with the second highest earthquake intensity in the world because Indonesia is passed by the confluence of 3 tectonic plates, namely: the Indo-Australian Plate, the Eurasian Plate, and the Pacific Plate. Earthquakes occur due to the movement of tectonic plates that move freely and interact with each other. The impact of the earthquake includes: Tsunami, collapsed buildings, fires, rock collapses, and ground cracks. The purpose of writing this study is to determine the intensity of earthquakes that occur on the island of Java using the K-Means Clustering method. The earthquake data used in this study uses magnitude, depth, and geographical location parameters obtained from existing data. The K-Means Clustering method is used to group earthquake data into several clusters based on their seismic characteristics. The result of this study is to identify seismic zones with similar earthquake intensity. From the results of the cluster obtained, it was used to make data visualization of areas with high earthquake intensity using GeoMap

This work aims to expand the knowledge of the area of data analysis through persistence homology and representations of directed graphs. To be specific, we looked for how we can analyze homology cluster groups using agglomerative Hierarchical Clustering algorithms and methods. Additionally, the Wine data, which is offered in R studio, was analyzed using various cluster algorithms such as Hierarchical Clustering, K-Means Clustering, and PAM Clustering. The goal of the analysis was to find out which cluster's method is proper for a given numerical dataset. We tried to find the agglomerative hierarchical clustering method by testing the data that will be the optimal clustering algorithm among these three; K-Means, PAM, and Random Forest methods. By comparing each model's accuracy value with cultivar coefficients, we concluded that K-Means methods are the most helpful when working with numerical variables. On the other hand, PAM clustering and Gower with Random Forest are the most beneficial approaches when using categorical variables. These tests can determine the optimal number of clustering groups, given the data set, and by doing the proper analysis. Using those the project, we can apply our method to several industrial areas such that clinical, business, and others. For example, people can make different groups based on each patient who has a common disease, required therapy, and other things in the clinical society. Additionally, people can expect to get several clustered groups based on the marginal profit, marginal cost, or other economic indicators for the business area.

Large-scale feature selection is one of the most important fields in the big data domain that can solve real data problems, such as bioinformatics, where it is necessary to process huge amount of data. The efficiency of existing feature selection algorithms significantly downgrades, if not totally inapplicable, when data size exceeds hundreds of gigabytes, because most feature selection algorithms are designed for centralized computing architecture. For that, distributed computing techniques, such as MapReduce can be applied to handle very large data. Our approach is to scale the existing method for feature selection, Kmeans clustering and Signal to Noise Ratio (SNR) combined with optimization technique as Binary Particle Swarm Optimization (BPSO). The proposed method is divided into two stages. In the first stage, we have used parallel Kmeans on MapReduce for clustering features, and then we have applied iterative MapReduce that implement parallel SNR ranking for each cluster. After, we have selected the top ranked feature from each cluster. The top scored features from each cluster are gathered and a new feature subset is generated. In the second stage, the new feature subset is used as input to the proposed BPSO based on MapReduce which provides an optimized feature subset. The proposed method is implemented in a distributed environment, and its efficiency is illustrated through analyzing practical problems such as biomarker discovery.

Floods are natural events or phenomena that can cause environmental damage, loss of property, psychological effects, and death or casualties. One way to control flooding non-structurally is by mapping areas that are prone to flooding. This study builds a geographic-based information system to map flood-prone areas in Manado City using the K-Means Clustering algorithm. The main objective of this research is to identify and map areas with a high risk of flooding using spatial data. Slope, land cover type, soil type, water discharge (discharge), and rainfall are independent variables that will be used and processed using the K-Means Clustering algorithm. There are four clusters in the mapping results of flood-prone areas, namely: high vulnerability, medium vulnerability, low vulnerability, and not vulnerable. By using the K-Means method, the results obtained are Paal Dua and Wenang sub-districts are high-vulnerability groups, followed by Mapanget, Tuminting, and Singkil subdistricts with medium vulnerability groups. Tikala District is the only area with low vulnerability. Meanwhile, Bunaken, Sario, Wanea, and Malayayang sub-districts are areas that are not potentially prone to flooding.

Employee discipline is one of the key success factors in a company. Work discipline has an important role in the formation of a positive work environment. One of the things that shows employee discipline is the time of attendance. Attendance time is usually recorded at the time the employee enters and leaves. Disciplinary information can be mapped into several groupings so that it is easy for decision makers to read. One of the computational methods that can perform data mapping is the K-Means Clustering method. The K-Means Clustering method can group data based on their characteristics. In this study, attendance data were analyzed using the K-Means method to obtain disciplinary groupings. The number of Clusters is calculated using the elbow method, 3 Clusters are obtained which are the best Cluster choices, namely Clusters 0, 1, and 2. The data analysis process shows Cluster 2 is the Cluster with the best level of discipline. From the analysis, it shows that the K-Means Clustering method can classify data based on employee discipline. Based on these results, decision makers can be helped in assessing employee discipline at Universita Harapan Bangsa using the disciplinary data grouping that has been made.

Background/Objectives: Data Mining has been used to analyze large datasets and establish useful classification and patterns in the datasets. The efficient analysis of data in different format becomes a challenging work. Methods/Statistical Analysis: This work proposed a novel Soil Profile Feature Reduction Model using Principal Component Analysis for data reduction. The proposed model uses the method of k-Means clustering and PC Aapproach for feature reduction which initially applies PCA to acquire reduced uncorrelated attributes showing maximal eigenvalues in the dataset with minimum loss of information. Again proposed model uses k-Means on the PCA reduced dataset to find out discriminative features that will be the most sufficient ones for classification. Findings: The weight by PCA generates attribute weights of the soil profile dataset using a component created by the PCA. The component is mentioned by the component number parameter. The normalize weight parameter is usually set to true to spread the weights between 0 and 1. The attribute weights reflect the relevance of the attributes with respect to the class attribute. The higher weight of an attribute is more relevant, it is considered. This is a combination of clustering approach with feature reduction to get a minimal set attributes relating a suitably high accuracy in describing the original features. The result of clustering is same after reducing the attributes using PCA. The experimental results prove that proposed model is reducing number of initial attributes, reducing computational complexity and improving predictive accuracy in High Dimensional Datasets. Applications/Improvements: The same soil profile feature is implemented by using the other techniques instead of PCA algorithm in future.

With the rapid development of artificial intelligence in recent years, the research on image processing, text mining, and genome informatics has gradually deepened, and the mining of large-scale databases has begun to receive more and more attention. The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra-high data dimensions, the number of samples available for analysis is too small, resulting in the production of high-dimensional small sample data. High-dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high-dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF-RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high-dimensional small sample data sets.