scholarly journals Precision Marketing Method of E-Commerce Platform Based on Clustering Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bei Zhang ◽  
Luquan Wang ◽  
Yuanyuan Li
Keyword(s):  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Cluster Center ◽  
Traditional Model ◽  
Clustering Method ◽  
Jaccard Distance ◽  
The Core ◽  
User Groups ◽  
Correlation Rules ◽  
Behavior Characteristics

In user cluster analysis, users with the same or similar behavior characteristics are divided into the same group by iterative update clustering, and the core and larger user groups are detected. In this paper, we present the formulation and data mining of the correlation rules based on the clustering algorithm through the definition and procedure of the algorithm. In addition, based on the idea of the K-mode clustering algorithm, this paper proposes a clustering method combining related rules with multivalued discrete features (MDF). In this paper, we construct a method to calculate the similarity between users using Jaccard distance and combine correlation rules with Jaccard distances to improve the similarity between users. Next, we propose a clustering method suitable for MDF. Finally, the basic K-mode algorithm is improved by the similarity measure method combining the correlation rule with the Jaccard distance and the cluster center update method which is the ARMDKM algorithm proposed in this paper. This method solves the problem that the MDF cannot be effectively processed in the traditional model and demonstrates its theoretical correctness. This experiment verifies the correctness of the new method by clustering purity, entropy, contour, and other indicators.

An Earthquake Clustering Method Based on Soft Distance Calculations

2013 ◽  
Vol 411-414 ◽  
pp. 1104-1107
Author(s):  
Jun Xia Chai ◽  
Dao Hua Liu
Keyword(s):  
Cluster Analysis ◽  
Strong Earthquake ◽  
Clustering Algorithm ◽  
Cluster Center ◽  
Analysis Method ◽  
Clustering Methods ◽  
Clustering Method ◽  
Accurate Calculation ◽  
Cluster Analysis Method ◽  
Data Source

The traditional cluster analysis method based on the true distance is not conducive to the accurate calculation of earthquake different fault rupture propagation and healing rate. This paper proposed and gave a new clustering method based on soft distance calculations. The clustering process based on soft distance calculations, the calculation method for soft distances and the specific clustering algorithm based on soft distances are given. For the real sample points of strong earthquake as a data source, we use this clustering method and other traditional clustering methods to cluster and analysis the data source, and analysis results have showed that the clustering method obtained the same cluster center with the earth stress field evolution, so this method has objective truth. The cluster analysis method for the earthquake fault zones in the accurate calculation of the next strong earthquake provides a good basis for the calculations.

A Novel Locality Sensitive K-Means Clustering Algorithm based on Core Clusters

2013 ◽  
Vol 321-324 ◽  
pp. 1939-1942
Author(s):  
Lei Gu
Keyword(s):  
Clustering Algorithm ◽  
Experimental Results ◽  
Clustering Method ◽  
Neighborhood Graph ◽  
The Core ◽  
Random Samples

The locality sensitive k-means clustering method has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random samples are employed for the initial centers. In this paper, an initialization method based on the core clusters is used for the locality sensitive k-means clustering. The core clusters can be formed by constructing the σ-neighborhood graph and their centers are regarded as the initial centers of the locality sensitive k-means clustering. To investigate the effectiveness of our approach, several experiments are done on three datasets. Experimental results show that our proposed method can improve the clustering performance compared to the previous locality sensitive k-means clustering.

A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information

2011 ◽  
pp. 133-150
Author(s):  
Zhang Xiaodan ◽  
Hu Xiaohua ◽  
Xia Jiali ◽  
Zhou Xiaohua ◽  
Achananuparp Palakorn
Keyword(s):  
Clustering Algorithm ◽  
Document Clustering ◽  
Biomedical Literature ◽  
Semantic Relationship ◽  
Clustering Method ◽  
Graph Representations ◽  
The Core ◽  
Document Cluster ◽  
Clustering Approach ◽  
Global And Local

In this article, we present a graph-based knowledge representation for biomedical digital library literature clustering. An efficient clustering method is developed to identify the ontology-enriched k-highest density term subgraphs that capture the core semantic relationship information about each document cluster. The distance between each document and the k term graph clusters is calculated. A document is then assigned to the closest term cluster. The extensive experimental results on two PubMed document sets (Disease10 and OHSUMED23) show that our approach is comparable to spherical k-means. The contributions of our approach are the following: (1) we provide two corpus-level graph representations to improve document clustering, a term co-occurrence graph and an abstract-title graph; (2) we develop an efficient and effective document clustering algorithm by identifying k distinguishable class-specific core term subgraphs using terms’ global and local importance information; and (3) the identified term clusters give a meaningful explanation for the document clustering results.

scholarly journals Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yang Lei ◽  
Dai Yu ◽  
Zhang Bin ◽  
Yang Yang
Keyword(s):  
Breast Cancer ◽  
Clustering Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
User Behavior ◽  
Clustering Method ◽  
High Dimensions ◽  
Swarm Optimization ◽  
The Core ◽  
Business Goals ◽  
Business Relation

Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor’s knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive K-means clustering method to improve the user’s satisfactions towards the result. The core of this method is to get the user’s feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user’s business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user’s requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.

scholarly journals Fuzzy Rough C-Mean Based Unsupervised CNN Clustering for Large-Scale Image Data

2018 ◽  
Vol 8 (10) ◽  
pp. 1869 ◽  
Author(s):  
Saman Riaz ◽  
Ali Arshad ◽  
Licheng Jiao
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Main Idea ◽  
Image Data ◽  
Training Image ◽  
Stochastic Gradient Descent ◽  
Cluster Center ◽  
Clustering Method

Deep learning has been well-known for a couple of years, and it indicates incredible possibilities for unsupervised learning of representations with the clustering algorithm. The forms of Convolution Neural Networks (CNN) are now state-of-the-art for many recognition and clustering tasks. However, with the perpetual incrementation of digital images, there exist more and more redundant, irrelevant, and noisy samples which cause CNN running to gradually decrease, and its clustering accuracy decreases concurrently. To conquer these issues, we proposed an effective clustering method for a large-scale image dataset which combines CNN and a Fuzzy-Rough C-Mean (FRCM) clustering algorithm. The main idea is that first a high-level representation, learned by multi-layers of CNN with one clustering layer, produce the initial cluster center, then during training image clusters, and representations, are updating jointly. FRCM is utilized to update the cluster centers in the forward pass, while the parameters of proposed CNN are updated by the backward pass based on Stochastic Gradient Descent (SGD). The concept of the rough set of lower and boundary approximations deal with uncertainty, vagueness, and incompleteness in cluster definition, and fuzzy sets enable efficient handling of overlapping partitions in the noisy environment. The experiment results show that the proposed FRCM based unsupervised CNN clustering method is better than the standard K-Mean, Fuzzy C-Mean, FRCM and also other deep-learning-based clustering algorithms on large-scale image data.

scholarly journals Classifying regencies and cities on human development index dimensions: Application of K-Means cluster analysis

2021 ◽  
Vol 5 (2) ◽  
pp. 913-918
Author(s):  
Nurhasanah Nurhasanah ◽  
Nany Salwa ◽  
Lyra Ornila ◽  
Amiruddin Hasan ◽  
Martahadi Mardhani
Keyword(s):  
Cluster Analysis ◽  
Human Development ◽  
Human Development Index ◽  
Development Process ◽  
Clustering Algorithm ◽  
Development Plan ◽  
Clustering Method ◽  
Development Index ◽  
The Third

The Human Development Index (HDI) is a measurement that analyzes a region's development in improving human development. The government's development plan aims to create a successful and peaceful life. The unbalanced development in every regency and city in Indonesia is a typical issue during the development process. It may also be shown that the HDI level changes across regencies and cities in Indonesia. This research aims to identify Indonesian regencies and cities based on HDI indices. K-Means clustering algorithm is the clustering method adopted. The results of the analysis formed 4 clusters. The first cluster consisted of 20 regencies with a low average HDI indicator. The second cluster consisted of 148 regencies and cities with an average HDI indicator is medium. The third cluster consisted of 88 regencies and cities with an average HDI indicator. The fourth cluster consists of 258 regencies and cities with high HDI indicators.

scholarly journals METODE k-MEDOIDS CLUSTERING DENGAN VALIDASI SILHOUETTE INDEX DAN C-INDEX (Studi Kasus Jumlah Kriminalitas Kabupaten/Kota di Jawa Tengah Tahun 2018)

2019 ◽  
Vol 8 (2) ◽  
pp. 161-170
Author(s):  
Milla Alifatun Nahdliyah ◽  
Tatik Widiharih ◽  
Alan Prahutama
Keyword(s):  
Cluster Analysis ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Distance Measures ◽  
Cluster Center ◽  
Silhouette Width ◽  
Central Java ◽  
Silhouette Index ◽  
Optimal Cluster

The k-medoids method is a non-hierarchical clustering to classify n object into k clusters that have the same characteristics. This clustering algorithm uses the medoid as its cluster center. Medoid is the most centrally located object in a cluster, so it’s robust to outliers. In cluster analysis the objects are grouped by the similarity. To measure the similarity, it can be used distance measures, euclidean distance and cityblock distance. The distance that is used in cluster analysis can affect the clustering results. Then, to determine the quality of the clustering results can be used the internal criteria with silhouette width and C-index. In this research the k-medoids method to classify of regencies/cities in Central Java based on type and number of crimes. The optimal cluster at k= 4 use euclidean distance, where the silhouette index= 0,3862593 and C-index= 0,043893. Keywords: Clustering, k-Medoids, Euclidean distance, Cityblock distance, Silhouette index, C-index, Crime

scholarly journals APPLICATION OF CLUSTERING METHOD IN THE RBF NEURAL NETWORKS

2001 ◽  
Vol 1 ◽  
pp. 257
Author(s):  
Pēteris Grabusts
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Significant Role ◽  
Mathematical Description ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Classification Algorithms ◽  
Rbf Neural Networks ◽  
Clustering Method

This paper describes one of classification algorithms, cluster analysis, that plays a significant role in the implementation of learning algorithm as applied to RBF-type artificial mural networks. The mathematical description of the K-means clustering algorithm is given and its implementation is demonstrated by experiment.

scholarly journals Protein Clustering in Formation of Falciparum Plasmodium using Soft Regularized-Markov Clustering Algorithm

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Hafizh Amrullah ◽  
Syamsuddin Wisnubroto
Keyword(s):  
Plasmodium Falciparum ◽  
Protein Interaction ◽  
Execution Time ◽  
Clustering Algorithm ◽  
Interaction Network ◽  
Cluster Center ◽  
Clustering Method ◽  
Large Protein ◽  
Markov Clustering ◽  
Inflation Parameter

AbstractProtein has an important role in our life. Every protein interacts with other proteins, DNA, and other molecules. It forms a very large protein interaction networks. We need clustering method to analyze it. Soft Regularized Markov Clustering (SR-MCL) algorithm is one of clustering method to reduce the weakness of Regularized Markov Clustering and Markov Clustering.  In this research, SR-MCL will be applied using OpenMP.  In every thread, SR-MCL is run using inflation parameter r = 2, 3, and 4. The simulation results show that, based on the fastest execution time and the smallest iteration, the parameter r = 2 produces the best cluster with 40 iterations and execution time is 613 seconds. The cluster centers obtained are 49 clusters with the largest cluster center is the XPO1 protein that interacts with 662 proteins, and 17 protein pairs that interact with each other. Therefore, the XPO1 is a very influential protein in Plasmodium Falciparum.Keywords: SR-MCL Algorithm, Protein Interaction Network, Plasmodium Falciparum. AbstrakProtein memiliki peranan yang sangat penting dalam kehidupan. Setiap  protein berinteraksi  dengan  protein-protein  lain,  DNA,  dan  molekul-molekul  lainnya, sehingga  terbentuklah  jaringan  interaksi  protein  yang  berukuran  sangat  besar. Untuk memudahkan dalam menganalisisnya, diperlukan metode clustering. Algoritma  Soft  Regularized  Markov  Clustering  (SR-MCL)  yang  merupakan pengembangan metode clustering untuk mengurangi kelemahan dari Regularized Markov  Clustering  dan Markov  Clustering.  Pada  penelitian  ini,  SR-MCL  akan diterapkan  menggunakan  OpenMP,  yaitu  setiap  thread  menjalankan  SR-MCL dengan  parameter  inflasi  r  =  2,  3,  dan  4.  Hasil simulasi menunjukkan bahwa, berdasarkan waktu eksekusi tercepat dan iterasi terkecil, cluster terbaik diperoleh ketika r = 2 yang menghasilkan 40 iterasi dengan waktu eksekusi 613 detik. Pusat cluster adalah protein XPO1 yang berinteraksi dengan 662 protein dan 17 pasangan protein yang saling berinteraksi satu dengan lainnya. Oleh karena itu, protein XPO1 adalah protein yang sangat berpengaruh dalam pembentukan Plasmodium Falciparum.Kata kunci: Algoritma SR-MCL, Jaringan Interaksi Protein, Plasmodium Falciparum.

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.

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