Exploiting geotagged resources for spatial clustering on social network services

Jung, Jason J.

doi:10.1002/cpe.3634

Cited by 20 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future research, we will study the query contextualization of merging spatio‐temporal information and perform experiments using big date . Since this work has exploited only a limited amount of imbalance dataset, we are planning to apply dataset balance approach with a large scale dataset from real‐world applications.…”

Section: Discussionmentioning

confidence: 99%

Spatio‐temporal query contextualization for microtext retrieval in social media

Park

Lee

Jung

2018

Concurrency and Computation

View full text Add to dashboard Cite

Summary Generally, texts on the social media (eg, Twitter and Facebook) are too short (microtexts) to understand the meaning and to search for relevant texts. It is difficult for the conventional information retrieval systems to conduct the searching tasks. Thereby, in this paper, we propose a novel approach on query contextualization by integrating all possible microtexts by considering spatio‐temporal contexts. The proposed approach consists of two steps, which are (i) to understand and process microtexts in social media and (ii) to reformulate the queries for searching for relevant microtexts in these social media. To evaluate the performance of the query contextualization approach, microtexts from Twitter have been collected during 4 months.

show abstract

Section: Discussionmentioning

confidence: 99%

Spatio‐temporal query contextualization for microtext retrieval in social media

Park

Lee

Jung

2018

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Moreover, techniques for automatically discovering story from the personal history are also considered to study [21]. In the end, we may extend the storification to further topics except the personal history (e.g., politics and social networking services [9,12,19]). …”

Section: Concluding Remarks and Future Workmentioning

confidence: 99%

Serendipity-based storification: from lifelogging to storytelling

Jung

Hong

Nguyễn

2016

Multimed Tools Appl

View full text Add to dashboard Cite

Storification is a theoretical technique which aims to construct the underlying relationships from discrete information for packaging them into a logical structure. In this paper, we focus on proposing the definition of serendipity-based storification in the personal history which is the combination of two-step processes: i) discovering hidden stories in the personal history and ii) representing stories using visualization techniques for easily grasping the information. MyMovieHistory Hong & Jung (Cybern Syst 46 (1-2), 69- 83 2015 ) is used as the case study to demonstrate the effect of storification through detecting and presenting patterns in real personal data. The results can be utilized in helping people easily memorize and comprehend their histories. Moreover, additional benefits (e.g., reminding the pass, predicting the future, and communicating who you are) can be gained through the use of storification in the personal history.

show abstract

“…3,4 For us to implement the prediction models accurately, it is necessary to analyze both domain knowledge and data. 1,2 Many AI-based prediction models, which use machine learning, data mining, databases, and statistical methods, are being proposed.…”

Section: Introductionmentioning

confidence: 99%

“…3,4 For us to implement the prediction models accurately, it is necessary to analyze both domain knowledge and data. Since majority class prediction is easy, its accuracy seems to be also high.…”

mentioning

confidence: 99%

“…Such prediction models based on state-of-the-art techniques are being applied in many fields, and there is a progressive increase in their industrial value. 3,4 For us to implement the prediction models accurately, it is necessary to analyze both domain knowledge and data. In addition, there is an increase in demand for obtaining useful knowledge from the collected data, and therefore, active research is being conducted on prediction models that are suitable for specific domains.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Particle swarm optimization–deep belief network–based rare class prediction model for highly class imbalance problem

Kim

Han

Lee

2017

Concurrency and Computation

View full text Add to dashboard Cite

Rare class imbalance problems, which involve the classification of minority or rare class, are difficult, because the size of the rare class is smaller than the majority class. Since majority class prediction is easy, its accuracy seems to be also high. However, the minority classes cannot be accurately predicted, and for this reason, when the prediction model performance is evaluated by considering only the accuracy, it does not indicate whether the model can predict the minority classes. Therefore, a rare class prediction technique is required. In this study, a rare class prediction model is proposed for minority class prediction. In addition, a dataset of a semiconductor manufacturing process with class imbalance problems was used to create a fault detection model. This prediction model uses data preprocessing to build the characteristics and data set required by the rare classes. To distinguish the rare classes related to the required characteristics, we used standard deviation and Euclidean distance to perform the feature selection. In addition, a particle swarm optimization-deep belief network was applied to create a classifier. The model proposed in this research presents outstanding performance and is appropriate for highly class imbalance problems. KEYWORDSclass imbalance problem, deep belief network, feature selection, particle swarm optimization, rare class classification INTRODUCTIONBecause of the issues with dig data and the development of deep learning techniques, the methods for building prediction models are in the spotlight. 1,2 Many AI-based prediction models, which use machine learning, data mining, databases, and statistical methods, are being proposed. Such prediction models based on state-of-the-art techniques are being applied in many fields, and there is a progressive increase in their industrial value. 3,4 For us to implement the prediction models accurately, it is necessary to analyze both domain knowledge and data. In addition, there is an increase in demand for obtaining useful knowledge from the collected data, and therefore, active research is being conducted on prediction models that are suitable for specific domains. 5,6 Thus, the importance of classification prediction techniques for class imbalance problems including class distribution, which is 1 of the main issues in the field of data mining, is increasing. 7-9 When the classes are balanced (balanced class), the ratios of the classes to be predicted are evenly distributed.Thus, by learning the data, a balanced predictive model that can predict all the classes can be generated. In the imbalance problem, the ratio of the category to be predicted is different. In this case, a classification prediction model that can predict only a specific class (rare class or majority class) is generated. For example, in the semiconductor manufacturing process, although most of the produced wafers are regular products, there is small probability for the production of irregular products. Therefore, a rare class prediction method is required to pre...

show abstract

Exploiting geotagged resources for spatial clustering on social network services

Cited by 20 publications

References 20 publications

Spatio‐temporal query contextualization for microtext retrieval in social media

Spatio‐temporal query contextualization for microtext retrieval in social media

Serendipity-based storification: from lifelogging to storytelling

Particle swarm optimization–deep belief network–based rare class prediction model for highly class imbalance problem

Contact Info

Product

Resources

About