Initial Centroid Selection Optimization for K-Means with Genetic Algorithm to Enhance Clustering of Transcribed Arabic Broadcast News Documents

Maghawry, Ahmed; Omar, Yasser M. K.; Badr, Amr

doi:10.1007/978-3-319-67621-0_8

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…As the final clustering result depends on the quality of the randomly selected initial centroids. Many techniques were introduced to neutralize the algorithms sensitivity to the initially selected centroids including the initial centroid selection optimization technique (ICSO) [15].…”

Section: B the K-means Clustering Algorithmmentioning

confidence: 99%

Automated Market Analysis by RFMx Encoding Based Customer Segmentation using Initial Centroid Selection Optimized K-means Clustering Algorithm

Maghawry¹,

Alqassed²,

Awad³

et al. 2021

IJCI. International Journal of Computers and Information

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Market analysis including customer segmentation is one of the most important approaches utilized by business owners to analyze customer behavior. Such analysis can provide significant insights and decision support for businesses. Multiple research effort was conducted for market analysis including the Recency, Frequency and Monetary analysis (RFM) in addition to many variations including RFD, RFE, RFM-I and RFMTC. In this research a methodology is proposed to utilize the intermediate vector representation of the introduced RFMx for machine learning toward high precision automatic customer segmentation. In this methodology there's no need to calculate the actual final RFMx score. The RFMx technique introduces a multimonetary model where each monetary value is assigned different weight to suite the business targets of business owners. The proposed model allowed for finely tuned market analyses on product type or service type level. The results showed significant clustering results that lead to automatic customer segmentation without the need to calculate the final RFMx score.

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Section: B the K-means Clustering Algorithmmentioning

confidence: 99%

Automated Market Analysis by RFMx Encoding Based Customer Segmentation using Initial Centroid Selection Optimized K-means Clustering Algorithm

Maghawry¹,

Alqassed²,

Awad³

et al. 2021

IJCI. International Journal of Computers and Information

View full text Add to dashboard Cite

show abstract

“…The literature presents many applications that employ text clustering for different purposes. For instance, some of the text applications employed were Tweets clustering [3], identifying crime patterns on the news [4], document clustering for scientific texts using citation contexts [5], short text clustering using Wikipedia as an additional knowledge source [6], automatic topic clustering of the transcribed speech documents [7], Arabic web pages clustering and annotation using semantic class features [8], clustering for semantically related words [9], multilingual corpora clustering [10], clustering of DNA texts [11]. Other applications include data compression, image processing, visualization, exploratory data analysis, pattern recognition, and time series prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring bigram character features for Arabic text clustering

AbuZeina¹

2019

Turk J Elec Eng & Comp Sci

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The vector space model (VSM) is an algebraic model that is widely used for data representation in text mining applications. However, the VSM poses a critical challenge, as it requires a high-dimensional feature space. Therefore, many feature selection techniques, such as employing roots or stems (i.e. words without infixes and prefixes, and/or suffixes) instead of using complete word forms, are proposed to tackle this space challenge problem. Recently, the literature shows that one more basic unit feature can be used to handle the textual features, which is the twoneighboring character form that we call microword. To evaluate this feature type, we measure the accuracy of the Arabic text clustering using two feature types: the complete word form and the microword form. Hence, the microword is two consecutive characters which are also known as the Bigram character feature. In the experiment, the principal component analysis (PCA) is used to reduce the feature vector dimensions while the k-means algorithm is used for the clustering purposes. The testing set includes 250 documents of five categories. The entire corpus contains 54,472 words, whereas the vocabulary contains 13,356 unique words. The experimental results show that the complete word form score accuracy is 97.2% while the two-character form score is 96.8%. In conclusion, the accuracies are almost the same; however, the two-character form uses a smaller vocabulary as well as less PCA subspaces. The study experiments might be a significant indication of the necessity to consider the Bigram character feature in the future text processing and natural language processing applications.

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“…Several distance-based algorithms have been adopted to assign objects to appropriate clusters, such as identifying disease using K-means and artificial neural network (ANN) 11,12 , fuzzy Cmeans, and multi K-means 13 . Nonetheless, finding the best initial clustering centroid and avoiding becoming stuck at the local optima are the challenges of the traditional algorithms 14 . An unsupervised approach using clustering can identify several diseases, which is promising in diagnosing strange diseases or incomprehensible behavior when no enough information is available 11,12 .…”

Section: Introductionmentioning

confidence: 99%

Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering

Al-Behadili

2022

Baghdad Sci.J

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Among the metaheuristic algorithms, population-based algorithms are an explorative search algorithm superior to the local search algorithm in terms of exploring the search space to find globally optimal solutions. However, the primary downside of such algorithms is their low exploitative capability, which prevents the expansion of the search space neighborhood for more optimal solutions. The firefly algorithm (FA) is a population-based algorithm that has been widely used in clustering problems. However, FA is limited in terms of its premature convergence when no neighborhood search strategies are employed to improve the quality of clustering solutions in the neighborhood region and exploring the global regions in the search space. On these bases, this work aims to improve FA using variable neighborhood search (VNS) as a local search method, providing VNS the benefit of the trade-off between the exploration and exploitation abilities. The proposed FA-VNS allows fireflies to improve the clustering solutions with the ability to enhance the clustering solutions and maintain the diversity of the clustering solutions during the search process using the perturbation operators of VNS. To evaluate the performance of the algorithm, eight benchmark datasets are utilized with four well-known clustering algorithms. The comparison according to the internal and external evaluation metrics indicates that the proposed FA-VNS can produce more compact clustering solutions than the well-known clustering algorithms.

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Initial Centroid Selection Optimization for K-Means with Genetic Algorithm to Enhance Clustering of Transcribed Arabic Broadcast News Documents

Cited by 6 publications

References 25 publications

Automated Market Analysis by RFMx Encoding Based Customer Segmentation using Initial Centroid Selection Optimized K-means Clustering Algorithm

Automated Market Analysis by RFMx Encoding Based Customer Segmentation using Initial Centroid Selection Optimized K-means Clustering Algorithm

Exploring bigram character features for Arabic text clustering

Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering

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