Evaluation of Partitioning Clustering Algorithms for Processing Social Media Data in Tourism Domain

Renjith, Shini; Sreekumar, A.; Jathavedan, M.

doi:10.1109/raics.2018.8635080

Cited by 25 publications

(17 citation statements)

References 4 publications

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“…Four new data sets were used for performance comparison for the first time. They are grid with stable and unstable situation (from energy field), 33 buddymove holiday, 34 and tripadviser holiday (from tourism field) 35 data sets. Table 6 gives the information about these new data sets.…”

Section: Resultsmentioning

confidence: 99%

Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

Altay

Alataş

2021

Concurrency and Computation

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In some practical situations, new computational methods are required for appropriately representing systems and their variables with inaccuracies, uncertainties, or variability. The chaos numbers seem to efficiently represent a set or range of values with lower and upper bounds for variables. In this article, a new chaos-enhanced representation scheme that is based on the notion of chaos numbers is proposed for evolutionary optimization methods. As a first application of chaos-based new encoding type, it is integrated into the novel hybrid intelligent optimization method proposed that is adapted as numerical association rules miner for the first time. The proposed hybrid method can also be used for complex types of search and optimization problems.The method is designed as a multiobjective rule miner that simultaneously handles different conflicting objectives and finds the accurate and comprehensible rules automatically. Based on the chaotic encoding, the proposed method easily and effectively adjusts the intervals of the attributes and automatically mines the rules without any preprocess. The performance of the proposed method was tested in real quantitative data sets and results were compared with the other association rule mining methods.According to the obtained results, the proposed method seems promising with respect to different metrics.

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Section: Resultsmentioning

confidence: 99%

Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

Altay

Alataş

2021

Concurrency and Computation

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“…In this section, we compare the performance of the proposed method with those of existing methods over six datasets from the UCI Machine Learning Repository: adult [27], car [5], winequality-red, white [8], googletrip-review, and tripadvisor-review [34]. Profile of the used datasets are summarized in Table 2.…”

Section: Experiments With Real-world Datasetsmentioning

confidence: 99%

Pre-Training Acquisition Functions by Deep Reinforcement Learning for Fixed Budget Active Learning

Taguchi

Hino

Kameyama

2021

Neural Process Lett

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There are many situations in supervised learning where the acquisition of data is very expensive and sometimes determined by a user’s budget. One way to address this limitation is active learning. In this study, we focus on a fixed budget regime and propose a novel active learning algorithm for the pool-based active learning problem. The proposed method performs active learning with a pre-trained acquisition function so that the maximum performance can be achieved when the number of data that can be acquired is fixed. To implement this active learning algorithm, the proposed method uses reinforcement learning based on deep neural networks as as a pre-trained acquisition function tailored for the fixed budget situation. By using the pre-trained deep Q-learning-based acquisition function, we can realize the active learner which selects a sample for annotation from the pool of unlabeled samples taking the fixed-budget situation into account. The proposed method is experimentally shown to be comparable with or superior to existing active learning methods, suggesting the effectiveness of the proposed approach for the fixed-budget active learning.

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“…This approach can be known based on the activities, interests, and opinions of tourists [8]. Thus, tourists will visit tours based on the available facilities and any information about the journey [6]. This information is useful and can be used as a reference for visiting tours.…”

Section: Introductionmentioning

confidence: 99%

“…Thus that in the field of tourism can be used as a business in the field of services and products [3], [4]. These reasons resulted in an area that would increase the potential for tourism development [5] as well as tourist attractions in East Asia [6]. Improvement can be made by knowing the potential that exists in the region.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy-AHP approach using Normalized Decision Matrix on Tourism Trend Ranking based-on Social Media

Saifullah

2019

Jurnal Informatika

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Evaluation of Partitioning Clustering Algorithms for Processing Social Media Data in Tourism Domain

Cited by 25 publications

References 4 publications

Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

Pre-Training Acquisition Functions by Deep Reinforcement Learning for Fixed Budget Active Learning

Fuzzy-AHP approach using Normalized Decision Matrix on Tourism Trend Ranking based-on Social Media

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