CLUSTERING CATEGORICAL DATA USING k-MODES BASED ON CUCKOO SEARCH OPTIMIZATION ALGORITHM

Lakshmi, K.; Visalakshi, N. Karthikeyani; Shanthi, S. Anita; Parvathavarthini, S

doi:10.21917/ijsc.2017.0218

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…Distance is either entropy [41] or Hamming distance [43]. Other algorithms have been proposed to improve optimization of the k-modes objective function without repeated reinitialization, including a tabu search [50], a genetic algorithm [51], a partical swarm optimizer [52], an artificial bee colony optimizer [39] and a Cuckoo search [53]. Most global optimizers and some initializers employ the traditional k-modes algorithms within their approach, and hence could benefit from use of the OTQT or OT algorithms.…”

Section: B Resultsmentioning

confidence: 99%

An Efficient $k$-modes Algorithm for Clustering Categorical Datasets

Dorman,

Maitra

2020

Preprint

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Mining clusters from datasets is an important endeavor in many applications. The k-means algorithm is a popular and efficient distribution-free approach for clustering numerical-valued data but can not be applied to categorical-valued observations. The k-modes algorithm addresses this lacuna by taking the k-means objective function, replacing the dissimilarity measure and using modes instead of means in the modified objective function. Unlike many other clustering algorithms, both k-modes and kmeans are scalable, because they do not require calculation of all pairwise dissimilarities. We provide a fast and computationally efficient implementation of k-modes, OTQT, and prove that it can find superior clusterings to existing algorithms. We also examine five initialization methods and three types of K-selection methods, many of them novel, and all appropriate for k-modes. By examining the performance on real and simulated datasets, we show that simple random initialization is the best intializer, a novel K-selection method is more accurate than two methods adapted from k-means, and that the new OTQT algorithm is more accurate and almost always faster than existing algorithms.

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Section: B Resultsmentioning

confidence: 99%

An Efficient $k$-modes Algorithm for Clustering Categorical Datasets

Dorman,

Maitra

2020

Preprint

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“…K-Modes merupakan algoritma clustering yang sering digunakan untuk mengelompokan data kategorik karena mudah diimplementasikan dan efisien untuk menangani data dalam jumlah besar [15]. Algoritma k-Modes menjamin konvergensi, artinya algoritma tersebut dapat memberikan hasil yang pasti.…”

Section: Pendahuluanunclassified

Analisis cluster data latar belakang ekonomi mahasiswa untuk rekomendasi penentuan uang kuliah tunggal dengan model K-Modes

Wirawan,

Prasetyawan

2023

infotech

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Latar belakang ekonomi mahasiswa merupakan salah satu faktor yang berpengaruh dalam keberhasilan seorang mahasiswa dalam menyelesaikan kuliahnya. Pengelompokan mahasiswa berdasarkan latar belakang ekonomi dapat digunakan untuk identifikasi kemampuan ekonomi mahasiswa. Penelitian ini bertujuan untuk pengelompokan data latar belakang ekonomi mahasiswa berdasarkan atribut-atribut di dalamnya, seperti penghasilan per kapita, status kepemilikan rumah, penggunaan daya listrik, jumlah mobil, jumlah motor, biaya pulsa dan internet, serta jaminan biaya pendidikan menggunakan algoritma k-Modes untuk rekomendasi dalam penentuan Uang Kuliah Tunggal (UKT). K-Modes digunakan dalam clustering ini karena k-Modes dapat menangani data kategorik dengan baik. Dalam proses clustering dengan menggunakan k-Modes, metode Elbow digunakan untuk mencari jumlah cluster yang paling optimal. Dari proses clustering, data latar belakang mahasiswa dikelompokan menjadi 3 cluster. Cluster pertama memiliki karakteristik dengan latar belakang ekonomi relatif rendah, cluster kedua memiliki karakteristik latar belakang ekonomi sedang, dan cluster ketiga memiliki karakteristik latar belakang ekonomi tinggi. Hasil dari dari proses analisis cluster tersebut selanjutnya digunakan sebagai rekomendasi dalam penentuan UKT.

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“…Accuracy: Accuracy refers to a measure of how well a model's predictions match the modeled reality. The classifi cation accuracy of the test data can be calculated by dividing the number of correctly classifi ed objects by the total number of objects (Lakshmi, K., Visalakshi, N. K., Shanthi, S., & Parvathavarthini, S., 2017;Tatbul, N., Lee, T. J., Zdonik, S., Alam, M., & Gottschlich, J., 2018).…”

Section: Fig 5: An Example Of a Pattern Generated By A Genetic Algori...mentioning

confidence: 99%

Traffic Flow Prediction Based on Vanet Data by Combining Artificial Neural Network and Genetic Algorithm

Tavasolian,

Afzali

2023

AzJHPC

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In many developing countries, predicting traffic flow is one of the solutions to prevent congestion on highways and routes, and the intelligent transportation system is considered one of the solutions to problems related to transportation and traffic. Knowledge of the predicted situation for traffic flow is essential in traffic management and informing passengers. This research presents a short-term intelligent transportation traffic flow forecasting model, which first examines how traffic forecasting can improve the performance of intelligent transportation system applications. Then the method and basic concepts of traffic flow forecasting are introduced, and the two main categories of forecasting, statistical models and machine learning-based forecasting methods (supervised and unsupervised) are discussed. Finally, a method based on machine learning using a genetic algorithm is Presented. The prediction was used as a powerful method for the mathematical modeling of traffic data in the proposed genetic algorithm method to select important traffic data features and neural networks for classification. The simulation and results presented in this research show a 3 percent improvement in traffic flow prediction with the proposed method, which uses SVM as a classifier in the primary method, and the simulation of this method has output a value of 93.6, But the suggested method has an output of 96.6

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CLUSTERING CATEGORICAL DATA USING k-MODES BASED ON CUCKOO SEARCH OPTIMIZATION ALGORITHM

Cited by 11 publications

References 20 publications

An Efficient $k$-modes Algorithm for Clustering Categorical Datasets

An Efficient $k$-modes Algorithm for Clustering Categorical Datasets

Analisis cluster data latar belakang ekonomi mahasiswa untuk rekomendasi penentuan uang kuliah tunggal dengan model K-Modes

Traffic Flow Prediction Based on Vanet Data by Combining Artificial Neural Network and Genetic Algorithm

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