Data Mining Methods: K-Means Clustering Algorithms

Annas, Mohammad; Wahab, Siti Norida

doi:10.34306/ijcitsm.v3i1.122

Cited by 3 publications

(2 citation statements)

References 15 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main advantage of decision trees is their ability to simplify complex decision-making processes, allowing decision-makers to understand problem solutions better. These algorithms perform calculations in data processing but have their calculation methods in producing output [35].…”

Section: Resultsmentioning

confidence: 99%

Utilization of Big Data in Libraries by Using Data Mining

Niham,

Elle,

Yuriah

et al. 2023

IJCITSM

View full text Add to dashboard Cite

This article analyzes extensive data libraries and how they benefit when completed. This research has results, namely library data can be called big data if the data fulfills the three characteristics of big data: volume, speed, and variety. Data mining can process big data, for example, by associations that create links between objects. If an extensive data library contains borrowed data, this means being able to establish connections between borrowed collections. Librarians, in particular, can use the results of this comprehensive data work to decide how to proceed. Therefore, it is essential to play a role in improving the library. This research aims to increase discussion between readers and librarians about using Big Data, especially in libraries. The benefit of this research is to broaden the knowledge of readers and librarians about how to utilize big data. The research method used is library research which analyzes writings from various sources.

show abstract

Section: Resultsmentioning

confidence: 99%

Utilization of Big Data in Libraries by Using Data Mining

Niham,

Elle,

Yuriah

et al. 2023

IJCITSM

View full text Add to dashboard Cite

show abstract

“…To calculate the accuracy value (7), it is found in equation, precision equation ( 8), recall equation (9). Besides using the confusion matrix, whether the prediction results are good or bad, a classification model can also use the Receiver Operating Characteristic (ROC) [29] and dan Area Under the Curve (AUC) [30]. Accuracy = (TP+TN)/(TP+TN+FP+FN) (7) Precision = (TP)/(TP+FP) (8) Recall = (TP) / (TP+FN) (9) Where TP= True Positive, TN=True Negative, FP=False Positive and FN = False Negative.…”

Section: Discussionmentioning

confidence: 99%

Sentiment Analysis of Bjorka Hacker Using the Naive Bayes and C.45 Algorithms

Priatna,

A’ini,

Warta

et al. 2023

conferenceseries

View full text Add to dashboard Cite

In 2023, Indonesia was again devastated by a hacker known as Bjorka. Bjorka did not act just once or twice; every time, Bjorka made the entire Indonesian population proud. The 19 million BPJS Employment data belonging to the Indonesian people that Bjorka hacked is the BPJS Employment data belonging to the Indonesian people that Bjorka hacked. Since the release of the Bjorka story, there has been a surge in the number of people criticizing it on social media, particularly Facebook, so the criticism or opinions can be used to conduct sentiment analysis. Based on this, developing a method that can automatically classify beliefs into positive and negative categories through sentiment analysis is necessary. The sentiment analysis process begins with data preprocessing, followed by keyword analysis using the TF-IDF method, algorithm development, and analysis of classification results. The data classification methods used in this study are Naive Bayes and C4.5. The data will be analyzed using text mining and classified using the Naive Bayes and C4.5 algorithms. Based on the results of the tests, the best classification was achieved by Nave Bayes, with a score of 70 percent for the C4.5 algorithm and 68 percent for the C4.5 algorithm. The Nave Bayes algorithm can predict up to 70% data transmission rates for both positive and negative signals.

show abstract

An Improved K-Means Algorithm Based on Contour Similarity

Zhao,

Bao,

et al. 2024

Mathematics

View full text Add to dashboard Cite

The traditional k-means algorithm is widely used in large-scale data clustering because of its easy implementation and efficient process, but it also suffers from the disadvantages of local optimality and poor robustness. In this study, a Csk-means algorithm based on contour similarity is proposed to overcome the drawbacks of the traditional k-means algorithm. For the traditional k-means algorithm, which results in local optimality due to the influence of outliers or noisy data and random selection of the initial clustering centers, the Csk-means algorithm overcomes both drawbacks by combining data lattice transformation and dissimilar interpolation. In particular, the Csk-means algorithm employs Fisher optimal partitioning of the similarity vectors between samples for the process of determining the number of clusters. To improve the robustness of the k-means algorithm to the shape of the clusters, the Csk-means algorithm utilizes contour similarity to compute the similarity between samples during the clustering process. Experimental results show that the Csk-means algorithm provides better clustering results than the traditional k-means algorithm and other comparative algorithms.

show abstract

Data Mining Methods: K-Means Clustering Algorithms

Cited by 3 publications

References 15 publications

Utilization of Big Data in Libraries by Using Data Mining

Utilization of Big Data in Libraries by Using Data Mining

Sentiment Analysis of Bjorka Hacker Using the Naive Bayes and C.45 Algorithms

An Improved K-Means Algorithm Based on Contour Similarity

Contact Info

Product

Resources

About