• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.381-389
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• 2022

With the collection of huge volumes of text, image, audio, video or combinations of these, in a word multimedia data, the need to explore them in order to discover possible new, unexpected and possibly valuable information for decision making was born. Starting from the already existing data mining, but not as its extension, multimedia mining appeared as a distinct field with increased complexity and many characteristic aspects. Later, the concept of big data was extended to multimedia, resulting in multimedia big data, which in turn attracted the multimedia big data mining process. This paper aims to survey multimedia data mining, starting from the general concept and following the transition from multimedia data mining to multimedia big data mining, through an up-to-date synthesis of works in the field, which is a novelty, from our best of knowledge.

• Proceedings of the Korea Multimedia Society Conference
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• 2001.11a
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• pp.429-434
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• 2001

데이터(data)치 홍수와 정보의 빈곤이라는 환경에 처한 지금, 정보기술을 이용하여 데이터를 여과하고, 분석하며, 결과를 해석하는 자동화 된 데이터 분석 방안에 높은 관심을 가지게 되었으며, 데이터 마이닝(Data Mining))은 이러한 요구를 충족시키는 정보기술의 활용방법이다. 특히 데이터 마이닝(Data Mining)의 분류(Classification) 방법은 중요한 분야가 되고 있다. 분류 작업의 핵심은 어떻게 적당한 결정규칙(decision rule)을 정의하느냐에 달려 있는데 이를 위해 학습능력을 가지고 있는 알고리즘이 필요하다. 본 논문에서는 유전자 알고리즘(Genetic Algorithm)을 기반으로 하는 강건한 학습방법을 제시했으며, 이러한 학습을 통해 데이터 마이닝(Data Mining)의 분류시스템을 제안하였다.

• Journal of Korea Multimedia Society
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• v.5 no.3
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• pp.281-289
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• 2002

Few studies have been systematically pursued on a multimedia data mining in despite of the over-whelming amounts of multimedia data by the development of computer capacity, storage technology and Internet. Based on the preliminary image processing and content-based image retrieval technology, this paper presents the methods for discovering association rules from recurrent items with spatial relationships in huge data repositories. Furthermore, multimedia mining algorithm is proposed to find implicit association rules among objects of which content-based descriptors such as color, texture, shape and etc. are recurrent and of which descriptors have spatial relationships. The algorithm with recurrent items in images shows high efficiency to find set of frequent items as compared to the Apriori algorithm. The multimedia association-rules algorithm is specially effective when the collection of images is homogeneous and it can be applied to many multimedia-related application fields.

• The KIPS Transactions:PartD
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• v.9D no.1
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• pp.57-64
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• 2002

Few studies have been systematically pursued on a multimedia data mining in despite of the overwhelming amounts of multimedia data by the development of computer capacity, storage technology and Internet. Based on the preliminary image processing and content-based image retrieval technology, this paper presents the methods for discovering association rules from recurrent items with spatial relationships in huge data repositories. Furthermore, multimedia mining algorithm is proposed to find implicit association rules among objects of which content-based descriptors such as color, texture, shape and etc. are recurrent and of which descriptors have spatial relationships. The algorithm with recurrent items in images shows high efficiency to find set of frequent items as compared to the Apriori algorithm. The multimedia association-rules algorithm is specially effective when the collection of images is homogeneous and it can be applied to many multimedia-related application fields.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1739-1748
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• 2008

When we conduct a data mining procedure on sample data sources, several rules are generated. But some rules are redundant or logically disjoint and therefore they can be removed. We suggest a new rule minimization algorithm inspired from logic synthesis to improve comprehensibility and eliminate redundant rules. The method can merge several relevant rules into one based on data mining and logic synthesis without high loss of accuracy. In case of two or more rules are candidates to be merged, we merge the rules with the attribute having the lowest information gain. To show the proposed method could be a reasonable solution, we applied the proposed approach to a problem domain constructing user preferred ontology in anti-spam systems.

• Proceedings of the Korea Database Society Conference
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• 1997.10a
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• pp.215-269
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• 1997

Outline $\textbullet$ Introduction $\textbullet$ Multimedia - Types of Data - Motivation - Key issue - Hardware Products - Application Areas $\textbullet$ Agents - Rationale for Agents - Sedentary vs. Mobile - Functional Categories - Application Areas $\textbullet$ Data Mining - 2-D Framework for Data Mining Tools - Classification of Tool - Application Areas - Learning Methodologies ＊ Case Based Reasoning ＊ Neural Networks ＊ Statistical Learning： Orthogonal Arrays ＊ Multi-strategy Learning $\textbullet$ Case Study - Finbot $\textbullet$ Conclusion

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.5
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• pp.740-751
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• 2020

In the aquaculture industry, few studies are analyzing big data for intrinsic meaning. Fishcare Laboratory (www.fishcare.kr) diagnostic data from 2016-2018 was analyzed for scuticociliatosis (caused by Miamiensis avidus) outbreak patterns in cultured olive flounder Paralichthys olivaceus in Jeju, Korea. The scuticociliatosis monthly occurrence ratio is reported in the summary table after preparing and filtering the basic dataset model. Nonparametric test results suggest differences in the water temperature, body length, and weight between groups with and without scuticociliatosis. Data distribution visualization revealed that shorter body length and lighter weight increased the occurrence of scuticociliatosis. The association rule mining technique was applied to determine the primary clinical signs of mixed scuticociliatosis and bacterial infections. Venn diagrams were used to report clinical signs and suggest commonalities. These results may help diagnose and treat fish and provide a decision-making reference.

• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.777-784
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• 2009

Recently, the importance of geoCRM (geographic Customer Relationship Management) systems are growing rapidly. So, result of the recognition that their applications extend well beyond the traditional CRM systems with the advent of ubiquitous environment and generalized location based services. A majority of traditional CRM systems are either incapable of managing spatial data or are not user-friendly when doing so. On the other hand, the geoCRM systems can be built as providing the geographic-based functions about CRM, including spatial and market analyses and the visualization of customer data, etc. However, it lacks the specific model and implementation of the geoCRM systems, being caused by the incomprehension of needs, the absence of related standards and the difficulties of development, and so on. In this paper, we develop a new spatial analysis supporting system that to enhance productivity through the convenient use and management of spatial data. The functionality provided by our system includes a set of analysis functions based on data mining techniques which allow a user to affect powerful transformation on spatial data. Particularly, both spatial data and non-spatial attributes can be efficiently handled as an object through our OODBMS.

• Journal of Korea Multimedia Society
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• v.5 no.1
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• pp.120-127
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• 2002

The common problems found in the data mining methods current in use have following problems. First: It is ineffective in searching for frequent items due to changing of minimal support values. Second: It is not adaptable to occurring of unuseful relation rules. Third: It is very difficult to re-use preceding results while adding new transactions. In this paper, we introduce a new method named as SPM-IRG(Selective Patters Mining using item Relation Graph), that is designed to solve above listed problems. SPM-IRG method creates a frequent items using minimal support values obtained by investigating direct or indirect relation of all items in transaction. Moreover, the new method can minimize inefficiency of existing method by constructing frequent items using only the items that we are interested.

• Journal of Multimedia Information System
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• v.2 no.2
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• pp.233-240
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• 2015

Mobile devices are becoming increasingly sophisticated and the latest generation of smartphones now incorporates many diverse and powerful sensors. These sensors include acceleration sensor, magnetic field sensor, light sensor, proximity sensor, gyroscope sensor, pressure sensor, rotation vector sensor, gravity sensor and orientation sensor. The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper, we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity that a user is performing. To implement our system, we collected labeled accelerometer data from 10 users as they performed daily activities such as "phone detached", "idle", "walking", "running", and "jumping", and then aggregated this time series data into examples that summarize the user activity 5-minute intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users-just by having them carry cell phones in their pockets.