Clustering Mining Research Articles

Perbandingan Algoritma K-Means, X-Means Dan K-Medoids Untuk Klasterisasi Awak Kabin Lion Air

Lion Air is part of PT. Lion Group, which is a airline in Indonesia and a low-cost airline based in Jakarta, Lion Air throughout the year experienced an increase in its fleet and an increase in the number of flights, the greater the need for cabin crew, in addition to the recruitment process which must be selective, it is also necessary to monitor the crew. cabin crew so that cabin crew performance will continue to be well maintained so that several groups are formed called cabin crew monitoring groups, currently cabin crew clustering is carried out randomly so as to produce group members with different characteristics. Clustering should be computerized by utilizing data mining clustering, at the data processing stage by eliminating missing values and determining attributes, it produces 100 data, at the modeling stage the most optimum results obtained using the k-means algorithm are 4 clusters and 6 attributes are indicated by the Davies-value. The Bouldin Index (DBI) is 0.792, while the DBI value for the x-means algorithm is 0.812 and the k-medoids algorithm is 1.700 so that the k-means algorithm is the best algorithm in this study.

In silico genome mining of potential novel biosynthetic gene clusters for drug discovery from Burkholderia bacteria

As an emerging resource, Gram-negative Burkholderia bacteria were able to produce a wide range of bioactive secondary metabolites with potential therapeutic and biotechnological applications. Genome mining has emerged as an influential platform for screening and pinpointing natural product diversity with the increasing number of Burkholderia genome sequences. Here, for genome mining of potential biosynthetic gene clusters (BGCs) and prioritizing prolific producing Burkholderia strains, we investigated the relationship between species evolution and distribution of main BGC groups using computational analysis of complete genome sequences of 248 Burkholderia species publicly available. We uncovered significantly differential distribution patterns of BGCs in the Burkholderia phyla, even among strains that are genetically very similar. We found various types of BGCs in Burkholderia, including some representative and most common BGCs for biosynthesis of encrypted or known terpenes, non-ribosomal peptides (NRPs) and some hybrid BGCs for cryptic products. We also observed that Burkholderia contain a lot of unspecified BGCs, representing high potentials to produce novel compounds. Analysis of BGCs for RiPPs (Ribosomally synthesized and posttranslationally modified peptides) and a texobactin-like BGC as examples showed wide classification and diversity of RiPP BGCs in Burkholderia at species level and metabolite predication. In conclusion, as the biggest investigation in silico by far on BGCs of the particular genus Burkholderia, our data implied a great diversity of natural products in Burkholderia and BGC distributions closely related to phylogenetic variation, and suggested different or concurrent strategies used to identify new drug molecules from these microorganisms will be important for the selection of potential BGCs and prolific producing strains for drug discovery.

Re-classification of Streptomyces venezuelae strains and mining secondary metabolite biosynthetic gene clusters

SummaryStreptomyces species have attracted considerable interest as a reservoir of medically important secondary metabolites, which are even diverse and different between strains. Here, we reassess ten Streptomyces venezuelae strains by presenting the highly resolved classification, using 16S rRNA sequencing, MALDI-TOF MS protein profiling, and whole-genome sequencing. The results revealed that seven of the ten strains were misclassified as S. venezuelae species. Secondary metabolite biosynthetic gene cluster (smBGC) mining and targeted LC-MS/MS based metabolite screening of S. venezuelae and misclassified strains identified in total 59 secondary metabolites production. In addition, a comparison of pyrrolamide-type antibiotic BGCs of four misclassified strains, followed by functional genomics, revealed that athv28 is critical in the synthesis of the anthelvencin precursor, 5-amino-3,4-dihydro-2H-pyrrole-2-carboxylate (ADPC). Our findings illustrate the importance of the accurate classification and better utilization of misclassified Streptomyces strains to discover smBGCs and their secondary metabolite products.

College students’ Network behavior Using data mining and feature analysis

Teachers may use advanced analytics to rapidly and correctly understand undergraduate behavior trends, especially when it comes to identifying undergraduate groupings that need to be focused on at a later time. This study uses data mining cluster analysis to analyze the constituent behavior of 3,245 undergraduates in a specific level ‘B’ institution’s college network. According to the data, there are four different undergraduate groups with different Web access features, with 350 participants using the accomplishments and other variables of their success have an influence on these students. As a result of this research, we were able to collect data on undergraduate college network activity, which may be used to aid in the development of academic advising management.

Draft genome sequence of Staphylococcus agnetis 4244, a strain with gene clusters encoding distinct post-translationally modified antimicrobial peptides

Here we report the draft genome sequence of Staphylococcus agnetis 4244, a strain involved in bovine mastitis, and its ability to inhibit different species of antibiotic-resistant Gram-positive bacteria owing to bacteriocin production. An Illumina MiSeq platform was used for genome sequencing. De novo genome assembly was done using the A5-miseq pipeline. Genome annotation was performed by the RAST server, and mining of bacteriocinogenic gene clusters was done using the BAGEL4 and antiSMASH v.5.0 platforms. Investigation of the spectrum of activity of S. agnetis 4244 was performed on BHI agar by deferred antagonism assay. The total scaffold size was determined to be 2 511 708 bp featuring a G+C content of 35.6%. The genome contains 2431 protein-coding sequences and 80 RNA sequences. Genome analyses revealed three prophage sequences inserted in the genome as well as several genes involved in drug resistance and two bacteriocin gene clusters (encoding a thiopeptide and a sactipeptide) encoded on the bacterial chromosome. Staphylococcus agnetis 4244 was able to inhibit all 44 strains of antibiotic-resistant Gram-positive bacteria tested in this study, including vancomycin-resistant enterococci (VRE), methicillin-resistant Staphylococcus aureus (MRSA) and other antibiotic-resistant staphylococcal strains. This study emphasises the potential biotechnological application of this strain for production of bacteriocins that could be used in the food industry as biopreservatives and/or in medicine as alternative therapeutic options against VRE, MRSA, vancomycin-intermediate S. aureus and other antibiotic-resistant Gram-positive bacteria, including biofilm-forming isolates. It also provides some genetic features of the draft genome of S. agnetis 4244.

Genome mining of biosynthetic gene clusters intended for secondary metabolites conservation in actinobacteria

Evolution of genome sequencing technology, on the one hand, and advancement of computational genome mining tools, on the other hand, paves way for improvement in predicting secondary metabolites. In past, numerous efforts were made concerning genome mining for recognizing secondary metabolites within the genus, but only a negligible quantity of comparative genomic reports had carried out among species of different genera. In this study, we explored potential of 24 actinobacteria species belonging to the genera, including Streptomyces, Nocardia, Micromonospora, and Saccharomonospora, to traverse diversity and distribution of Biosynthetic Gene Clusters (BGCs). Investigating results obtained from antiSMASH (Antibiotics and Secondary Metabolites Analysis Shell), NaPDoS (Natural Product Domain Seeker), and NP.searcher revealed conservation of genus-specific gene clusters among various species. E.g., NAGGN (n-acetyl glutaminyl glutamine amide) is present in Micromonospora, furan in Nocardia, melanin, and lassopeptide occur in Streptomyces. Bioactive compounds like alkyl-O-dihydro geranyl methoxy hydroquinone, SapB, desferrioxamine E, 2-Methylisoborneol, mayamycin, cyclodipeptide synthase, diisonitrile, salinichelin, hopene, ectoine and isorenieratene are highly conserved among diverse genera. Furthermore, pharmacological activity of actinobacterial derived metabolites against bacterial and fungal pathogens were illustrated. We need to accomplish large-scale analysis of natural products, including various genera of actinobacteria to deliver comprehensive intuition to overcome antibiotic resistance.

A new clustering mining algorithm for multi-source imbalanced location data

In the era of big data, clustering based on multi-source data fusion has become a hot topic in data mining field. Existing studies mainly focus on fusion models and algorithms of data sets in the same domain, but few studies consider imbalanced data sets from different domains. Furthermore, studies on imbalanced data sets mostly focus on classification and less on clustering problems. Therefore, we propose a novel clustering algorithm for mining fused location data. This algorithm can deal with imbalanced data sets with large density differences, find clusters generated by the minority class data, and reduce the time complexity of the clustering process. Since current evaluation indices are not suitable for evaluating clustering results of imbalanced data sets, we present a new comprehensive evaluation metric used in the clustering validity judgment. Urban hotspots mining is used as an example, and the effectiveness of the proposed method is validated using GPS trajectory data from the transport domain and check-in data from the social network. The experimental results demonstrate that the performance of the proposed algorithm outperforms that of the state-of-the-art clustering algorithms, and it can simultaneously discover urban hotspots formed by the majority and minority class data.

Identification of Novel Modalities Through Bibliometric Analysis for Timely Development of Regulatory Guidance: A Case Study of T Cell Immunity.

Background: The mission of medicines regulatory agencies is to ensure the timely access of innovative products for patients to improve public health. Thus, regulators should foresee evolving technologies and build expertise prior to reviewing innovative products. Novel modalities and new classes of therapeutics in biological or cell-based products represent a regulatory challenge because of knowledge gaps, as exemplified by the unexpected cytokine release syndrome in the first-in-human clinical trial of the CD28 super-agonist. Meanwhile, recent treatments harnessing T cell co-signaling pathways provide an opportunity for investigation. Therefore, this study aimed to systematically identify and evaluate novel modalities for T cell immunity to assess the need for regulatory guidance.Methods: A PubMed search was carried out using the query, “immun* AND t lymph*” to select publications. Subsequently, a citation network was created, followed by clustering and text mining to identify the modalities and classes of therapeutics under development.Results and Discussion: Analysis of the top 20 clusters revealed research domains characterized by keywords such as immune checkpoint antibody, chimeric antigen receptor (CAR)-T cells, microbiota, exosome, regulatory T cells, unconventional T cells, and vaccines. After reviewing the pharmacological concepts, clinical trial information, and available guidance, we presented a perspective on the future development of guidance for these domains.Conclusion: Bibliometric analyses identified a set of innovative modalities targeted for drug development with which regulatory guidance is going to catch up. This strategy could help in the successful development of upcoming modalities to ensure readiness for clinical application as part of horizon scanning.

Economic Growth and E-Commerce: Potensial for Digitizing SMSEs in East Java

The social and economic impacts caused by Covid-19 pandemic have changed the way people live. The existence of social activities and shopping due to spread of Covid-19, resulted in switching from an offline system to an online system, including in the case that number of e-commerce increased during the pandemic. In 2020 number of e-commerce businesses in East Java Province grew to 90.31 percent from 19.92 percent in 2019. According to Liu (2013), number of e-commerce businesses will affect economic growth. Pattern of relationship between e-commerce and economic growth needs to be known for an appropriate policy so that both can continue to develop. Furthermore, it is necessary to segment and optimize aspects that support the development of e-commerce and the digitization of MSMEs. This study aims to analyze effect of e-commerce businesses on economic growth in Java, segmenting and optimizing aspects that support development of e-commerce in the locus of Java Island, especially East Java Province, and complete it with an analysis of big data on public response to digital economy in times of pandemic. Method used are a combination of 5 techniques at once, namely regression analysis, thematic map visualization, clustering, spatial analysis, and text mining. Results show that e-commerce affects economic growth of provinces in Java. E-commerce businesses need to continue to be optimized by improving the quality of HDI and internet networks, especially in priority areas for the development of East Java Province. Programs related to digital economy need to be encouraged to harmonize changes in social order with the ability of MSMEs to adapt to digitalization era.

DELVING INTO BLOOD TRANSFUSIONS DATA THROUGH DATA MINING: A STUDY OF MAIZBHANDARI SHAH EMDADIA BLOOD DONORS GROUP TO SELECT VOLUNTEER BLOOD DONORS EFFICIENTLY

The demand for blood transfusion is rising gradually. Therefore, volunteer blood donors are needed to save the lives of patients. There are lot of volunteer donors in every society. But, the main problem people face is finding such donors at the right time. To locate potential blood donors, we collected data from the Maizbhandari Shah Emdadia blood donors’ group, which consists of 700 active volunteer blood donors. This study aims to choose potential volunteer donors efficiently at the emergency time from the Maizbhandari Shah Emdadia blood donors’ group based on their past data. We have developed two models, namely descriptive and predictive models, using data mining techniques. The descriptive model analyzes data patterns and explores the donors’ behaviour. A data mining clustering algorithm was used to develop the descriptive model. The underlying factors of donors' intention to donate blood were identified and evaluated using this descriptive model. These factors were then utilized to develop the predictive model, which in turn assists to predict whether a donor will donate blood or not during an emergency. The findings of these two models will assist the clinical experts in locating potential volunteer blood donors within the shortest period and thus save valuable lives.

Neural Network Data Mining Clustering Optimization Algorithm

Data mining technology is an effective way to solve the problem of rich data and poor knowledge. Cluster analysis is an important content of data mining, including analysis ideas based on partition, hierarchy, density, grid and model. Neural network clustering is a typical clustering method based on model thinking. It is an organic combination of brain cognitive science and data mining. It has a strong theoretical connection with actual brain processing knowledge. This paper uses the K-means algorithm to optimize the neural network clustering data mining algorithm, and designs experiments to verify the neural network data mining clustering optimization algorithm proposed in this paper. The experimental research results in this paper show that on four UCI datasets, the mean MP values of the neural network data mining clustering optimization algorithms are 77.9 and 87.72, respectively, which are greater than the values of the other two algorithms. This paper also applies the algorithm to the study of the distribution of remaining oil. The algorithm has achieved obvious results in the cluster analysis of the degree of flooding.

BROCCOLI: overlapping and outlier-robust biclustering through proximal stochastic gradient descent

Matrix tri-factorization subject to binary constraints is a versatile and powerful framework for the simultaneous clustering of observations and features, also known as biclustering. Applications for biclustering encompass the clustering of high-dimensional data and explorative data mining, where the selection of the most important features is relevant. Unfortunately, due to the lack of suitable methods for the optimization subject to binary constraints, the powerful framework of biclustering is typically constrained to clusterings which partition the set of observations or features. As a result, overlap between clusters cannot be modelled and every item, even outliers in the data, have to be assigned to exactly one cluster. In this paper we propose Broccoli, an optimization scheme for matrix factorization subject to binary constraints, which is based on the theoretically well-founded optimization scheme of proximal stochastic gradient descent. Thereby, we do not impose any restrictions on the obtained clusters. Our experimental evaluation, performed on both synthetic and real-world data, and against 6 competitor algorithms, show reliable and competitive performance, even in presence of a high amount of noise in the data. Moreover, a qualitative analysis of the identified clusters shows that Broccoli may provide meaningful and interpretable clustering structures.

RETRACTED ARTICLE: Urban ecological environment investigation based on a cloud computing platform and optimization of computer neural network algorithm

The rapid development of Internet technology has contributed to the development of the Internet. The rapid expansion of the industrial application scale and the sharp increase of data generated by an industrial application and cloud computing have promoted the birth and development of cloud computing platform technology. Data analysis has become an important support for company decision-making; how to quickly and effectively extract useful information from large data sets is very important. Among them, clustering analysis and computer neural network algorithms are the most important methods of data mining. Due to the limitations of computer performance and programming models, traditional data mining methods cannot meet the efficiency and computing needs of a large amount of information. The development of cloud computing technology has opened up a new research field of cloud mining clustering analysis and neural network algorithm formation. The first research is the deployment of the Hadoop cluster based on Linux, and the novelty lies in the parallelization of the MapReduce cluster algorithm. Because there are many clustering algorithms, this paper starts with the K-means clustering algorithm and optimizes the earlier algorithm according to the MapReduce programming model. Through the text clustering process of wine data set in the UCI database, the parallel algorithm is applied to the Hadoop cloud computing platform. Experiments show that the parallel K-means clustering algorithm in MapReduce can significantly improve the execution speed. This paper applies it to the urban ecological environment survey in China, clarifies the relationship between various factors of urban environmental governance, expands the content of urban environmental governance in China, and improves the ecological environment of Chinese cities.

Novel hybrid geometric data perturbation technique by means of sampling data intervals

Privacy preserving data mining has much importance in the data analysis because it maintains confidential or highly sensitive data in the purpose of data analysis. Many perturbation techniques are developed, but do not perform well. There are used for different purposes in various techniques. Though, the reconstruction of original data can be done from the samples of perturbed data. It does not always preserve the distance that exists in the original data samples. During reconstruction of the original data, some of the information is loosed which is again a complex and time consuming task for traditional data mining algorithm. Therefore, a novel hybrid data perturbation technique is introduced that overcomes the above addressed issues. The proposed paper includes a novel data perturbation technique to hybridize the traditional Geometric Data Perturbation Technique along with the Euclidean distance method to preserve privacy. It obtains the distorted or perturbed data which is sampled at different intervals depending on the levels of privacy. The proposed approach attempts to reduce the tradeoffs that persists between privacy levels, accuracy and information loss. The experimental results for this approach is implemented using real world bench mark datasets downloaded from UCI Repositories using three different data mining clustering algorithms. It is shown that clustering accuracy is similar and fairly high for the perturbed data obtained from the proposed approach when matched with the original data set clustering algorithms. The analysis were shown for different privacy levels on different data samples.

LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing

Abstract Lane-level road cluster is a most representative phenomenon in road networks and is vital to spatial data mining, cartographic generalization, and data integration. In this article, a lane-level road cluster recognition method was proposed. First, the conception of lane-level road cluster and our motivation were addressed and the spatial characteristics were given. Second, a region growing cluster algorithm was defined to recognize lane-level road clusters, where constraints including distance and orientation were used. A novel moving distance (MD) metric was proposed to measure the distance of two lines, which can effectively handle the non-uniformly distributed vertexes, heterogeneous length, inharmonious spatial alignment, and complex shape. Experiments demonstrated that the proposed method can effectively recognize lane-level road clusters with the agreement to human spatial cognition.

Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations.

Heterogeneity among Alzheimer’s disease (AD) patients confounds clinical trial patient selection and therapeutic efficacy evaluation. This work defines separable AD clinical sub-populations using unsupervised machine learning. Clustering (t-SNE followed by k-means) of patient features and association rule mining (ARM) was performed on the ADNIMERGE dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Patient sociodemographics, brain imaging, biomarkers, cognitive tests, and medication usage were included for analysis. Four AD clinical sub-populations were identified using between-cluster mean fold changes [cognitive performance, brain volume]: cluster-1 represented least severe disease [+17.3, +13.3]; cluster-0 [−4.6, +3.8] and cluster-3 [+10.8, −4.9] represented mid-severity sub-populations; cluster-2 represented most severe disease [−18.4, −8.4]. ARM assessed frequently occurring pharmacologic substances within the 4 sub-populations. No drug class was associated with the least severe AD (cluster-1), likely due to lesser antecedent disease. Anti-hyperlipidemia drugs associated with cluster-0 (mid-severity, higher volume). Interestingly, antioxidants vitamin C and E associated with cluster-3 (mid-severity, higher cognition). Anti-depressants like Zoloft associated with most severe disease (cluster-2). Vitamin D is protective for AD, but ARM identified significant underutilization across all AD sub-populations. Identification and feature characterization of four distinct AD sub-population “clusters” using standard clinical features enhances future clinical trial selection criteria and cross-study comparative analysis.

Movie recommendation system using clustering mining with Python

The research we are pursuing here is about building a system which we will use and mine a movie review database using concepts of statistics like Euclidean Distance algorithm. This will help us determine similar review according to the ratings. What we plan on achieving through this approach is to analyze the reviews to suggest a suitable critic according to one’s preferences and one’s taste. We will do this according to the review we select and our algorithm which we use upon it. This algorithm will be the key to find those similar reviews. We are going to use Python programming to program this experiment. We have chosen to pursue this because of not the reviewers being incorrect or correct but merely different according to their preferences and their lean towards a certain genre and we know that a reviewer should also match the taste of the person who want to feel benefited of the review itself. So, we plan to make system that could help with that.

Implementasi Data Mining untuk mengelompokkan Pasien Rawat Jalan dan Rawat Inap Asuransi Kesehatan

Patient Visits Outpatient and inpatient insurance at Class C Hospitals is increasing from year to year. Increased visits to insurance patients will have an impact on the inpatient and outpatient health services provided. From the increase in patient visits, the data owned by the hospital is increasingly abundant. The data can be used to explore knowledge, find certain patterns.
 To explore knowledge about Inpatient and Outpatient Insurance patients, data mining clustering techniques are used with the Self Organizing Map (SOM) algorithm using R Studio tools. Clustering technique with the implementation of the Self Organizing Map (SOM) algorithm is a technique for grouping data based on certain characteristics which are then mapped into areas that resemble map shapes. The CRISP-DM method is used in this study to perform the stages of the data mining process.
 The results obtained from the implementation of clustering with the Self Organizing Map (SOM) algorithm are obtained 2 clusters representing dense areas and non-congested areas. Dense areas are represented by Internal Medicine Clinic, Surgery Clinic, Eye Clinic, Hemodialysis, Melati Room, Orchid Room, Bougenville Room, Flamboyan Room. Non-crowded areas are represented by General Clinics, Dental Clinics, Obstetrics and Gynecology Clinics, Children's Clinics, Mawar Room and Soka Room

Chemical similarity methods for analyzing secondary metabolite structures

Abstract The relation that exists between the structure of a compound and its function is an integral part of chemoinformatics. The similarity principle states that “structurally similar molecules tend to have similar properties and similar molecules exert similar biological activities”. The similarity of the molecules can either be studied at the structure level or at the descriptor level (properties level). Generally, the objective of chemical similarity measures is to enhance prediction of the biological activities of molecules. In this article, an overview of various methods used to compare the similarity between metabolite structures has been provided, including two-dimensional (2D) and three-dimensional (3D) approaches. The focus has been on methods description; e.g. fingerprint-based similarity in which the molecules under study are first fragmented and their fingerprints are computed, 2D structural similarity by comparing the Tanimoto coefficients and Euclidean distances, as well as the use of physiochemical properties descriptor-based similarity methods. The similarity between molecules could also be measured by using data mining (clustering) techniques, e.g. by using virtual screening (VS)-based similarity methods. In this approach, the molecules with the desired descriptors or /and structures are screened from large databases. Lastly, SMILES-based chemical similarity search is an important method for studying the exact structure search, substructure search and also descriptor similarity. The use of a particular method depends upon the requirements of the researcher.

Data Mining and Risk Prediction Based on Apriori Improved Algorithm for Lung Cancer

Starting from medical big data, this article uses data mining technology to analyze and study the pathogenic factors of lung cancer based on the lung cancer electronic medical record data from the oncology department of the authoritative third grade A hospital for many years. With respect to the processing of huge data from electronic medical records for lung cancer, traditional serial Apriori algorithm has the disadvantages of scanning database frequently, running slowly and consuming large amount of memory resources. Therefore, an improved Apriori algorithm based on MapReduce distributed computing model of Hadoop platform is proposed. The experimental cluster and lung cancer data mining experiments show that the improved Apriori algorithm has higher execution efficiency and good system scalability in dealing with lung cancer big data, and can well mine the relationship between lung cancer and pathogenic factors, which has important guiding significance for assisting the clinical diagnosis and risk prediction of lung cancer.