scholarly journals An effective and efficient hierarchical K-means clustering algorithm

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772862 ◽  
Author(s):  
Jianpeng Qi ◽  
Yanwei Yu ◽  
Lihong Wang ◽  
Jinglei Liu ◽  
Yingjie Wang
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Hierarchical Optimization ◽  
Clustering Method ◽  
Number Of Clusters ◽  
Computation Cost ◽  
Optimization Principle ◽  
Pruning Strategy ◽  
Efficiency And Effectiveness ◽  
Synthetic Datasets

K-means plays an important role in different fields of data mining. However, k-means often becomes sensitive due to its random seeds selecting. Motivated by this, this article proposes an optimized k-means clustering method, named k*-means, along with three optimization principles. First, we propose a hierarchical optimization principle initialized by k* seeds ([Formula: see text]) to reduce the risk of random seeds selecting, and then use the proposed “top- n nearest clusters merging” to merge the nearest clusters in each round until the number of clusters reaches at [Formula: see text]. Second, we propose an “optimized update principle” that leverages moved points updating incrementally instead of recalculating mean and [Formula: see text] of cluster in k-means iteration to minimize computation cost. Third, we propose a strategy named “cluster pruning strategy” to improve efficiency of k-means. This strategy omits the farther clusters to shrink the adjustable space in each iteration. Experiments performed on real UCI and synthetic datasets verify the efficiency and effectiveness of our proposed algorithm.

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.

scholarly journals TCLUST: Trimming Approach of Robust Clustering Method

2014 ◽  
Vol 8 (4) ◽  
Author(s):  
Muhamad Alias Md. Jedi ◽  
Robiah Adnan
Keyword(s):  
Clustering Algorithm ◽  
Likelihood Function ◽  
R Package ◽  
Clustering Method ◽  
Number Of Clusters ◽  
Robust Clustering ◽  
Scatter Matrix ◽  
Group Assignment ◽  
Log Likelihood ◽  
Clustering Approach

TCLUST is a method in statistical clustering technique which is based on modification of trimmed k-means clustering algorithm. It is called “crisp” clustering approach because the observation is can be eliminated or assigned to a group. TCLUST strengthen the group assignment by putting constraint to the cluster scatter matrix. The emphasis in this paper is to restrict on the eigenvalues, λ of the scatter matrix. The idea of imposing constraints is to maximize the log-likelihood function of spurious-outlier model. A review of different robust clustering approach is presented as a comparison to TCLUST methods. This paper will discuss the nature of TCLUST algorithm and how to determine the number of cluster or group properly and measure the strength of group assignment. At the end of this paper, R-package on TCLUST implement the types of scatter restriction, making the algorithm to be more flexible for choosing the number of clusters and the trimming proportion.

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 A dynamic K-means clustering for data mining

2019 ◽  
Vol 13 (2) ◽  
pp. 521
Author(s):  
Md. Zakir Hossain ◽  
Md.Nasim Akhtar ◽  
R.B. Ahmad ◽  
Mostafijur Rahman
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Large Data ◽  
Threshold Value ◽  
Specific Pattern ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Number Of Clusters ◽  
Data Points

<span>Data mining is the process of finding structure of data from large data sets. With this process, the decision makers can make a particular decision for further development of the real-world problems. Several data clusteringtechniques are used in data mining for finding a specific pattern of data. The K-means method isone of the familiar clustering techniques for clustering large data sets.  The K-means clustering method partitions the data set based on the assumption that the number of clusters are fixed.The main problem of this method is that if the number of clusters is to be chosen small then there is a higher probability of adding dissimilar items into the same group. On the other hand, if the number of clusters is chosen to be high, then there is a higher chance of adding similar items in the different groups. In this paper, we address this issue by proposing a new K-Means clustering algorithm. The proposed method performs data clustering dynamically. The proposed method initially calculates a threshold value as a centroid of K-Means and based on this value the number of clusters are formed. At each iteration of K-Means, if the Euclidian distance between two points is less than or equal to the threshold value, then these two data points will be in the same group. Otherwise, the proposed method will create a new cluster with the dissimilar data point. The results show that the proposed method outperforms the original K-Means method.</span>

Rough ISODATA Algorithm

2013 ◽  
Vol 3 (4) ◽  
pp. 1-14 ◽  
Author(s):  
S. Sampath ◽  
B. Ramya
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Vital Role ◽  
Data Sets ◽  
Clustering Method ◽  
Data Set ◽  
Number Of Clusters ◽  
Real Life Data ◽  
Nonparametric Statistical

Cluster analysis is a branch of data mining, which plays a vital role in bringing out hidden information in databases. Clustering algorithms help medical researchers in identifying the presence of natural subgroups in a data set. Different types of clustering algorithms are available in the literature. The most popular among them is k-means clustering. Even though k-means clustering is a popular clustering method widely used, its application requires the knowledge of the number of clusters present in the given data set. Several solutions are available in literature to overcome this limitation. The k-means clustering method creates a disjoint and exhaustive partition of the data set. However, in some situations one can come across objects that belong to more than one cluster. In this paper, a clustering algorithm capable of producing rough clusters automatically without requiring the user to give as input the number of clusters to be produced. The efficiency of the algorithm in detecting the number of clusters present in the data set has been studied with the help of some real life data sets. Further, a nonparametric statistical analysis on the results of the experimental study has been carried out in order to analyze the efficiency of the proposed algorithm in automatic detection of the number of clusters in the data set with the help of rough version of Davies-Bouldin index.

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

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.

scholarly journals AK-means: an automatic clustering algorithm based on K-means

2015 ◽  
Vol 4 (2) ◽  
pp. 231 ◽  
Author(s):  
Omar Kettani ◽  
Faical Ramdani ◽  
Benaissa Tadili
Keyword(s):  
Data Mining ◽  
Fast Algorithm ◽  
Clustering Algorithm ◽  
Data Sets ◽  
Number Of Clusters ◽  
Correct Number ◽  
Standard Data ◽  
Exact Number ◽  
Automatic Clustering ◽  
Clustering Problems

<p>In data mining, K-means is a simple and fast algorithm for solving clustering problems, but it requires that the user provides in advance the exact number of clusters (k), which is often not obvious. Thus, this paper intends to overcome this problem by proposing a parameter-free algorithm for automatic clustering. It is based on successive adequate restarting of K-means algorithm. Experiments conducted on several standard data sets demonstrate that the proposed approach is effective and outperforms the related well known algorithm G-means, in terms of clustering accuracy and estimation of the correct number of clusters.</p>

scholarly journals Computational analysis of incremental clustering approaches for Large Data

2021 ◽  
Vol 15 ◽  
pp. 14-18
Author(s):  
Arun Pratap Singh Kushwah ◽  
Shailesh Jaloree ◽  
Ramjeevan Singh Thakur
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Computational Analysis ◽  
Large Data ◽  
Distance Functions ◽  
Spatial Density ◽  
Incremental Clustering ◽  
Clustering Method ◽  
Density Method ◽  
Incremental Approach

Clustering is an approach of data mining, which helps us to find the underlying hidden structure in the dataset. K-means is a clustering method which usages distance functions to find the similarities or dissimilarities between the instances. DBSCAN is a clustering algorithm, which discovers the arbitrary shapes & sizes of clusters from huge volume of using spatial density method. These two approaches of clustering are the classical methods for efficient clustering but underperform when the data is updated frequently in the databases so, the incremental or gradual clustering approaches are always preferred in this environment. In this paper, an incremental approach for clustering is introduced using K-means and DBSCAN to handle the new datasets dynamically updated in the database in an interval.

K-Means Algorithm: An Assessment

2015 ◽  
Vol 9 (1and2) ◽  
Author(s):  
Vijay Kumar
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Number Of Clusters ◽  
Clustering Technique ◽  
Nearest Neighbor Searching ◽  
Current Review ◽  
A Current

In data mining, clustering is a technique in which the set of objects are assigned to a group called clusters. Clustering is the most essential part of data mining. K-means clustering is the basic clustering technique and is most widely used algorithm. It is also known as nearest neighbor searching. It simply clusters the datasets into given number of clusters. Numerous efforts have been made to improve the performance of the K-means clustering algorithm. In this paper we have been briefed in the form of a review the work carried out by the different researchers using Kmeans clustering. We have discussed the limitations and applications of the K-means clustering algorithm as well. This paper presents a current review about the K means clustering algorithm.

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