Stochastic K-means algorithm for vector quantization

Kovesi, B.; Boucher, Jean‐Marc; Saoudi, Samir

doi:10.1016/s0167-8655(01)00021-6

Cited by 41 publications

(8 citation statements)

References 5 publications

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“…Centroid dapat dikatakan Convergent jika posisinya tidak berubah lagi selama proses iterasi. Perubahan posisi centroid juga dapat berhenti pada iterasi ke dengan threshold (Kovesi 2011).jika posisi tersebut telah diperbaharui oleh jarak yang lebih kecil .…”

Section: Preprosessing Dataunclassified

Segmentasi Pelanggan Internet Service Provider (ISP) Berbasis Pillar K-Means

Hadi¹

2019

jitika

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Perusahan penyedia layanan internet service provider (ISP) memiliki jumlah pelanggan yang sangat banyak dan beragam. Dengan semakin banyak dan beraamnya jumlah pelanggan peusahaan akan sulit untuk mengetahui tipe pelanggan yang dimiliki oleh perusahaan. Akibatnya perusahaan akan kesulitan menerapkan strategi pemasaran yang tepat kepada konsumen. Dalam paper ini digunakan metode pillar K-means untuk melakukan segmentasi pelanggan. Algoritma pilar k-means untuk melakukan segmentasi pelanggaran . Algoritma Pillar merupakan metode optimasi untuk menentukan centroid awal dalam algoritma K-Means. Dengan mengoptimasi centroid awal maka akan menghasilkan cluster yang lebih baik . setelah memperoleh hasil cluster yang optimal selanjutnya tipe pelanggaran dianalisis dengan menggunakan metode RFM (Recency, Frequency, Montetery). Hasil penelitian ini menunjukan bahwa pllar K-means mampu mengoptimasi hasil cluster : k = 4 dengan a = 0.5 dan b = 0.8 serta nilai silheoette 8 = 0.47103. Dari hasil segmentasi 150 pelangganan diperoleh tipe pelangganan yang terdiri Most Valuable Costmers (33) Most Growable Costomers (41), Migrators (23) dan Below Zero (53).

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Section: Preprosessing Dataunclassified

Segmentasi Pelanggan Internet Service Provider (ISP) Berbasis Pillar K-Means

Hadi¹

2019

jitika

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“…In the new framework, K-means clustering algorithm is used for optimization of codebook [22], [23]. It is performed by the following steps.…”

Section: ) Patch Level Featurementioning

confidence: 99%

“…2 shows the diagram of VQ in which the term "codebook" is a document used for implementing a code. It contains a lookup table for coding and decoding.In the new framework, K-means clustering algorithm is used for optimization of codebook [22], [23]. It is performed by the following steps.…”

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confidence: 99%

A Novel Framework Using Graph-Based Clustering for Dental X-Ray Image Search in Medical Diagnosis

Tuấn¹,

Sơn²

2016

IJET

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Abstract-Dental X-Ray image search is an important process in medical diagnosis for diagnosing exactly dental diseases of a patient. This problem is regarded as the matching of a dental X-Ray image with diseases patterns in the database. In this paper, we propose a novel framework using graph-based clustering for dental X-Ray image search. This framework firstly extracts dental features from an X-Ray image to a dental feature database and then uses a vector quantization algorithm to clarify the principal records from the database. Each record is now regarded as a node in a graph which is classified by a graph-based clustering algorithm according to the disease patterns. The dental X-Ray image is classified having disease or non-disease according to other disease patterns in the same group. The new method is experimental validated on a real dataset of 13 dental X-ray images taken from Hanoi Medical University, Vietnam at the period of 2014-2015. Three variants of the framework namely Prim spanning tree (GCP), Kruskal spanning tree (GCK), and Affinity Propagation Clustering (APC) has been implemented. The experimental results suggest the best variant in term of accuracy.Index Terms-Dental X-ray images, graph-based clustering, medical diagnosis, affinity propagation clustering. I. INTRODUCTIONA computerized medical diagnosis system is of great interest to clinicians for accurate decision making of possible diseases and treatments. Because of knowledge re-use and the capability to learn by examples, it has an important role to routine works of clinicians. In recent years, there have been many researches that aim to develop such the system. In 2012, Subgahata Chattopadhyay et al. [1] presented an application of Bayesian network to diagnose toothache. Kavitha et al. [2] used Support Vector Machine (SVM) to predict the osteoporosis from dental images. In 2014, Kantesh and Xu [3] proposed a fuzzy-based method to predict heart risk. Sutton [4] used Fuzzy Neighbor K-Nearest Neighbour (FKNN) method in different dentistry problems.In dentistry, dental X-Ray image search is the core process of medical diagnosis for diagnosing exactly dental diseases of a patient. This problem is regarded as the matching of a dental X-Ray image with diseases patterns in the database. If it is similar to a disease pattern then its status is 'disease'; otherwise is 'non-disease'. There have been some achievements in form of image searching based on graph modeling [5]- [7]. Based on these studies, we model the dental X-Ray image search in a graph and then use a graph-based clustering method to determine the cluster containing the new X-Ray image. From that, diagnosing information is determined via membership degrees of the cluster.In this paper, we propose a novel framework using graph-based clustering for dental X-Ray image search. The novel parts of this framework in comparison with the relevant researches are: 1) generate a dental feature database of dental X-ray images; 2) use a vector quantization algorithm to clarify the principal reco...

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“…Many works have been done to improve the k-means weaknesses [14,15]. However the basic algorithm is the most widely used one.…”

Section: Partitioningmentioning

confidence: 99%

A New Method For Clustering In Credit Scoring Problems

Gholamian¹,

Jahanpour²,

Sadatrasoul³

2013

J. Math. Computer Sci.

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Due to the recent financial crisis and regulatory concerns of Basel II, credit risk assessment has become one of the most important topics in the financial risk management. Quantitative credit scoring models are widely used to assess credit risk in financial institutions. In this paper we introduce Time Adaptive self organizing Map Neural Network to cluster creditworthy customers against non credit worthy ones. We test this Neural Network on Australian credit data set and compare the results with other clustering Algorithm's include K-means, PAM, SOM against different internal and external measures. TASOM has the best performance in clusters customers.

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Stochastic K-means algorithm for vector quantization

Cited by 41 publications

References 5 publications

Segmentasi Pelanggan Internet Service Provider (ISP) Berbasis Pillar K-Means

Segmentasi Pelanggan Internet Service Provider (ISP) Berbasis Pillar K-Means

A Novel Framework Using Graph-Based Clustering for Dental X-Ray Image Search in Medical Diagnosis

A New Method For Clustering In Credit Scoring Problems

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