OCA: Overlapping Clustering Application: Unsupervised Approach for Data Analysis

Danganan, Alvincent Egonia; Sison, Ariel M.; Medina, Ruji P.

doi:10.1109/iciibms.2018.8550020

Cited by 4 publications

(5 citation statements)

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“…Clustering algorithms collect primary information based on the attributes, characteristics, and diversity of that information to perform the clustering process by computing the least distance between the centroid of each cluster and the current elements to put into any cluster [20]. The determination of optimal number of clusters which is decided by the user is key weaknesses of K-means algorithm.…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

Food recommendation system based on nutritional needs of human beings and user preferences

Jasim

Hamid

2022

ijhs

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Introduction: Nowadays the food types became so diverse and complicated, so human needs+ professional assistance to make his best choices especially after foods became global parameter. Food Recommendation System is a smart system that provides the best suggestions to the beneficiaries to know the best choices to their needs. Moreover, the human activities and lifestyle are affected by another types of dietaries in other foods. There is need for everybody to know what the nutrition is he/she needs. So, this research responding to these needs. The goal of the proposed system is to propose a system that provides recommendations for foods that are rich in nutritional components that people need in their daily lives based on computational model and expert preferences. Objective: The research aims to design and implement a food recommendation system has the ability to coordinate both user preferences and data clustering techniques to produce high accuracy recommendations. Material and methods: The proposed method focuses on merging computational model and user preferences to give the user the best recommended list of food options. Clustering techniques are approaches used in Recommendation system applications to group different foods according to the similarities in nutrition values.

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Section: K-means Clustering Algorithmmentioning

confidence: 99%

Food recommendation system based on nutritional needs of human beings and user preferences

Jasim

Hamid

2022

ijhs

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“…In [8] and [31] the authors provide methods that would efficiently mine outliers in large datasets. Other recent studies have devised methods in clustering analysis that will prune or screen out outliers from the dataset such as Liu, et al [29], Barai and Dey [6], Gan and Nk [21] , Danganan et al [19] and Chagas et al [14] . For example, Yu, et al [35] proposed an outlier detection method to identify and eliminate outliers in the dataset forming an outlier-eliminated dataset (OED).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similarly, Danganan et al [19] proposed a modification of MCOKE [3] by incorporating a median absolute deviation (MAD) that measures any potential outliers in the dataset. The authors proposed a threephased approach in which the objects are ranked in ascending order and the distance of each object is calculated against MAD which is multiplied to a certain constant number determined by the user to obtain a decision value.…”

Section: Literature Reviewmentioning

confidence: 99%

“…While many studies focus on pruning and discarding the outliers to improve the classification process, rarely do we find algorithms that detect outliers simultaneously while performing clustering [19]. The K-means with outlier removal (KMOR) algorithm is similar to the standard K-means algorithm but introduces an outlier cluster (k+1) that takes into account objects that don't fit in the k defined clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

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OMCOKE: A Machine Learning Outlier-based Overlapping Clustering Technique for Multi-Label Data Analysis

Baadel

Thabtah²,

et al. 2022

IJCAI

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Clustering is one of the challenging machine learning techniques due to its unsupervised learning nature. While many clustering algorithms constrain objects to single clusters, K-means overlapping partitioning clustering methods assign objects to multiple clusters by relaxing the constraints and allowing objects to belong to more than one cluster to better fit hidden structures in the data. However, when datasets contain outliers, they can significantly influence the mean distance of the data objects to their respective clusters, which is a drawback. Therefore, most researchers address this problem by simply removing the outliers. This can be problematic especially in applications such as fraud detection or cybersecurity attacks risk analysis. In this study, an alternative solution to this problem is proposed that captures outliers and stores them on-the-fly within a new cluster, instead of discarding. The new algorithm is named Outlier-based Multi-Cluster Overlapping K-Means Extension (OMCOKE). Empirical results on real-life multi-label datasets were derived to compare OMCOKE's performance with other common overlapping clustering techniques. The results show that OMCOKE produced a better precision rate compared to the considered clustering algorithms. This method can benefit various stakeholders as these outliers could have real-life applications in cybersecurity, fraud detection, and the anti-phishing of websites.Povzetek: V tej študiji je predlagana alternativna rešitev (OMCOKE), ki zajame izstope in jih sproti shrani v novo gručo, namesto da bi jih odstranila.

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“…K-means is one of the most popular and oldest clustering techniques and can be applied even to large data sets [19]. The K-means algorithm gives a simple method to execute an approximate solution.…”

Section: K-means Algorithmmentioning

confidence: 99%

Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms

Jaafar

Gaata

Jasim

2020

IJEECS

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<p>The recommendation system is an intelligent system gives recommendations to users to discover the best interesting items. The purpose of this proposed recommendation system is to develop a system to find the best electrical devices according to weather conditions and user preferences. The proposed solution relies on the characteristics of electrical appliances and their suitability to weather conditions in any city. The proposed solution is the first recommendation system combines devices properties, weather conditions, and user preferences using a new combination of algorithms. The clustering algorithms are the most applicable in the field of recommendation system. The proposed solution relies on a combination of Elbow method, proposed modified K-means and Silhouette algorithm to find the best number of clusters before starting the clustering process. Then calculate the weights for each cluster and compare them with the weather weights to find the required clusters sorted from the near to far according to a computed threshold. The empirical results showed that the proposed solution demonstrated a 94% accuracy to match the characteristics of the recommended devices with the climatic characteristics of the region and user preferences. The accuracy is measured using Silhouette algorithm.</p>

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OCA: Overlapping Clustering Application: Unsupervised Approach for Data Analysis

Cited by 4 publications

References 0 publications

Food recommendation system based on nutritional needs of human beings and user preferences

Food recommendation system based on nutritional needs of human beings and user preferences

OMCOKE: A Machine Learning Outlier-based Overlapping Clustering Technique for Multi-Label Data Analysis

Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms

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