scholarly journals Dengue Disease Detection using K- Means, Hierarchical, Kohonen- SOM Clustering

2019 ◽  
Vol 8 (10) ◽  
pp. 904-907
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Research Work ◽  
Data Mining Algorithm ◽  
Clustering Method ◽  
Dengue Disease ◽  
Related Data ◽  
Som Algorithm ◽  
Som Clustering ◽  
Kohonen Som

Data Mining is the process of extracting useful information. Data Mining is about finding new information from pre-existing databases. It is the procedure of mining facts from data and deals with the kind of patterns that can be mined. Therefore, this proposed work is to detect and categorize the illness of people who are affected by Dengue through Data Mining techniques mainly as the Clustering method. Clustering is the method of finding related groups of data in a dataset and used to split the related data into a group of sub-classes. So, in this research work clustering method is used to categorize the age group of people those who are affected by mosquito-borne viral infection using K-Means and Hierarchical Clustering algorithm and Kohonen-SOM algorithm has been implemented in Tanagra tool. The scientists use the data mining algorithm for preventing and defending different diseases like Dengue disease. This paper helps to apply the algorithm for clustering of Dengue fever in Tanagra tool to detect the best results from those algorithms.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

Data Mining Clustering Algorithm Research and Application

2014 ◽  
Vol 926-930 ◽  
pp. 3608-3611 ◽  
Author(s):  
Yi Fan Zhang ◽  
Yong Tao Qian ◽  
Tai Yu Liu ◽  
Shu Yan Wu
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Dimensional Space ◽  
Data Mining Algorithm ◽  
Practical Application ◽  
Advantages And Disadvantages ◽  
Analysis Theory ◽  
Data Points

In this paper, first introduce data mining knowledge then focuses on the clustering analysis algorithms, including classification clustering algorithm, and each classification typical cluster analysis algorithms, including the formal description of each algorithm as well as the advantages and disadvantages of each algorithm also has a more detailed description. Then carefully introduce data mining algorithm on the basis of cluster analysis. And using cohesion based clustering algorithm with DBSCAN algorithm and clustering in consumer spending in two-dimensional space, 2,000 data points for each area, and get a reasonable clustering results, resulting in hierarchical clustering results valuable information, so as to realize the practical application of the algorithm and clustering analysis theory combined.

scholarly journals Association Analysis of Obesity/Overweight and Breast Cancer Using Data Mining Techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 60
Author(s):  
Mahsa Dehghani Soufi ◽  
Reza Ferdousi
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Hierarchical Clustering ◽  
Cancer Progression ◽  
Clustering Algorithm ◽  
Breast Cancer Incidence ◽  
Research Work ◽  
Model Assessment ◽  
Cancer Data ◽  
Data Mining Algorithms

Introduction: Growing evidence has shown that some overweight factors could be implicated in tumor genesis, higher recurrence and mortality. In addition, association of various overweight factors and breast cancer has not been extensively explored. The goal of this research was to explore and evaluate the association of various overweight/obesity factors and breast cancer, based on obesity breast cancer data set.Material and Methods: Several studies show that a significantly stronger association is obvious between overweight and higher breast cancer incidence, but the role of some overweight factors such as BMI, insulin-resistance, Homeostasis Model Assessment (HOMA), Leptin, adiponectin, glucose and MCP.1 is still debatable, So for experiment of research work several clinical and biochemical overweight factors, including age, Body Mass Index (BMI), Glucose, Insulin, Homeostatic Model Assessment (HOMA), Leptin, Adiponectin, Resistin and Monocyte chemo attractant protein-1(MCP-1) were analyzed. Data mining algorithms including k-means, Apriori, Hierarchical clustering algorithm (HCM) were applied using orange version 3.22 as an open source data mining tool.Results: The Apriori algorithm generated a list of frequent item sets and some strong rules from dataset and found that insulin, HOMA and leptin are two items often simultaneously were seen for BC patients that leads to cancer progression. K-means algorithm applied and it divided samples on three clusters and its results showed that the pair of andlt;Adiponectin, MCP.1andgt;  has the highest effect on seperation of clusters. In addition HCM was carried out and classified BC patients into 1-32 clusters to So this research apply HCM algorithm. We carried out hierarchical clustering with average linkage without purning and classified BC patients into 1–32 clusters in order to identify BC patients with similar charestrictics.Conclusion: These finding provide the employed algorithms in this study can be helpful to our aim.

scholarly journals Clustering Penerima Beasiswa Yayasan Untuk Mahasiswa Menggunakan Metode K-Means

2021 ◽  
Vol 5 (1) ◽  
pp. 258
Author(s):  
Bernadus Gunawan Sudarsono ◽  
Sri Poedji Lestari
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Scholarship Recipients

Grouping of scholarship recipients Scholarship assistance will be made based on the accumulated value using clustering where the scholarship recipients will be given scholarships with different amounts and sizes, because scholarships from foundations are limited and have levels of distribution. The division of groups to students who receive scholarships from foundations uses the clustering method of data mining where the function of clustering is a cluster or the task of grouping something is using the clustering algorithm approach, namely the K-means algorithm. The results of this clustering show that students based on their groups are divided into four groups based on the number of criteria, the results of the grouping show the number and decision of the foundation on granting foundation scholarships to students.

scholarly journals Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Kai Ji
Keyword(s):  
Data Mining ◽  
Communication Network ◽  
Big Data ◽  
Clustering Algorithm ◽  
Personal Information ◽  
Personal Communication ◽  
Communication Process ◽  
Data Mining Algorithm ◽  
Mining Algorithm ◽  
Wireless Personal Communication

Wireless personal communication network is easily affected by intrusion data in the communication process, resulting in the inability to ensure the security of personal information in wireless communication. Therefore, this paper proposes a malicious intrusion data mining algorithm based on legitimate big data in wireless personal communication networks. The clustering algorithm is used to iteratively obtain the central point of malicious intrusion data and determine its expected membership. The noise in malicious intrusion data is denoised by objective function, and the membership degree of communication data is calculated. The change factor of the neighborhood center of gravity of malicious intrusion data in wireless personal communication network is determined, the similarity between the characteristics of malicious intrusion data by using the Markov distance was determined, and the malicious intrusion data mining of wireless personal communication network supported by legal big data was completed. The experimental results show that the accuracy of mining malicious data is high and the mining time is short.

Research on Clustering Analysis Based on SOM

2013 ◽  
Vol 475-476 ◽  
pp. 968-971
Author(s):  
Hai Xue Liu ◽  
Rui Jun Yang ◽  
Wen Ju Li ◽  
Wan Jun Yu ◽  
Wei Lu
Keyword(s):  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Poor Quality ◽  
Original Algorithm ◽  
Space Model ◽  
Som Algorithm ◽  
Som Network ◽  
Som Clustering ◽  
Selection Of

In this paper, we present an improved text clustering algorithm. It not only maintains the self-organizing features of SOM network, but also makes up the disadvantages of the bad clustering effect caused by the inadequate selection of K-means algorithm. Firstly, data is preprocessed to form vector space model for subsequent process. Then, we analyze the features of original clustering algorithm and SOM algorithm, and plan an improved SOM clustering algorithm to overcome low stability and poor quality of original algorithm. The experimental results indicate that the improved algorithm has a higher accuracy and has a better stability, compared with the original algorithm.

An Improving Algorithm Based on SOM Clustering and its Applications

2013 ◽  
Vol 655-657 ◽  
pp. 1000-1004
Author(s):  
Chen Guang Yan ◽  
Yu Jing Liu ◽  
Jin Hui Fan
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Network Clustering ◽  
Self Organizing Map ◽  
Clustering Method ◽  
Map Algorithm ◽  
Som Neural Network ◽  
Simulation Results ◽  
Som Clustering ◽  
Selection Of

SOM (Self-organizing Map) algorithm is a clustering method basing on non-supervision condition. The paper introduces an improved algorithm based on SOM neural network clustering. It proposes SOM’s basic theory on data clustering. For SOM’s practical problems in applications, the algorithm also improved the selection of initial weights and the scope of neighborhood parameters. Finally, the simulation results in Matlab prove that the improved clustering algorithm improve the correct rate and computational efficiency of data clustering and to make the convergence speed better.

scholarly journals Penerapan Algoritma K-Means Clustering untuk Melihat Hubungan Kegiatan Tahfiz dengan Hasil Belajar

2020 ◽  
pp. 41-47
Author(s):  
Asri Hidayad ◽  
Sarjon Defit ◽  
S Sumijan
Keyword(s):  
Data Mining ◽  
Learning Outcomes ◽  
Clustering Algorithm ◽  
Student Learning Outcomes ◽  
Training Data ◽  
Data Mining Algorithm ◽  
Data Mining Technique ◽  
Grouping Method ◽  
Student Grades ◽  
And Training

The purpose of this study is to evaluate whether Tahfiz activities and learning outcomes are effective or not. The data processed in this study were data on tahfiz activities and data on student learning outcomes in class XI (eleven) totaling 42 data sourced from memorization of tahfiz, tahfiz grades, and student grades in Madrasah Aliyah Negeri 1 Bukittinggi. Based on the analysis of the data, this classification uses one of the methods of the Data Mining algorithm, K-Means Clustering. K-Means Clustering algorithm works based on the grouping method, In this data mining technique consists of data testing and training data with the input of the number of memorization of tahfiz, and the value of tahfiz as well as learning outcomes. The results of this study the school can determine how influential this activity tahfiz on student grades.

scholarly journals SVM Classifier on K-means Clustering Algorithm with Normalization in Data Mining for Prediction

2019 ◽  
Vol 7 (6) ◽  
pp. 29-34
Author(s):  
Vasu Deep ◽  
Himanshu Sharma
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Research Work ◽  
Initial Point ◽  
Computational Time ◽  
Svm Classifier ◽  
Point Selection ◽  
Cancer Dataset ◽  
Number Of Iterations

This work is belonging to K-means clustering algorithms classifier is used with this algorithm to classified data and Min Max normalization technique also used is to enhance the results of this work over simply K- Means algorithm. K-means algorithm is a clustering algorithm and basically used for discovering the cluster within a dataset. Here cancer dataset is used for this research work and dataset is classified in two categories – Cancer and Non-Cancer, after execution of the implemented algorithm with SVM and Normalization technique. The initial point selection effects on the results of the algorithm, both in the number of clusters found and their centroids. In this work enhance the k-means clustering algorithm methods are discussed. This technique helps to improve efficiency, accuracy, performance and computational time. Some enhanced variations improve the efficiency and accuracy of algorithm. The main of all methods is to decrees the number of iterations which will less computational time. K-means algorithm in clustering is most popular technique which is widely used technique in data mining. Various enhancements done on K-mean are collected, so by using these enhancements one can build a new proposed algorithm which will be more efficient, accurate and less time consuming than the previous work. More focus of this studies is to decrease the number of iterations which is less time consuming and second one is to gain more accuracy using normalization technique overall belonging to improve time and accuracy than previous studies.

scholarly journals DATA MINING DALAM PENGELOMPOKAN JENIS DAN JUMLAH PEMBAGIAN ZAKAT DENGAN MENGGUNAKAN METODE CLUSTERING K-MEANS (STUDI KASUS: BADAN AMIL ZAKAT KOTA BENGKULU)

2018 ◽  
Vol 1 (2) ◽  
pp. 211
Author(s):  
Prahasti Prahasti
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Clustering Algorithm ◽  
Test Results ◽  
Clustering Method ◽  
Center Point

Abstrack - This research applies data mining by grouping the types and recipients of zakat. The application is done by the k-means clustering algorithm where the data to be entered is grouped by education and type of work in the distribution of zakat. Then a cluster is formed using the centroid value to determine the closest center point of distance between data. In the k-means clustering algorithm data processing is stopped in the iteration count of the data has not changed (fixed data) from the data that has been grouped. The test is done by using the RapidMiner software experiment conducted by the k-means clustering method which consists of input units, data processing units and output units, k-means clustering grouping data 1-2-1-1, 1-2-1-2 and 3-4-3-4. The results obtained from these tests are grouping the distribution of zakat with each cluster not the same. The test results are displayed in slatter graph.  Keywords - Data Mining, K-Means Clusttering, Zakat

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