| 研究課題/領域番号 |
21K12034
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| 研究機関 | 会津大学 |
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研究代表者 |
Rage Uday・Kiran 会津大学, コンピュータ理工学部, 准教授 (20874324)
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| 研究分担者 |
是津 耕司 国立研究開発法人情報通信研究機構, ユニバーサルコミュニケーション研究所統合ビッグデータ研究センター, 研究センター長 (40415857)
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| 研究期間 (年度) |
2021-04-01 – 2025-03-31
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| キーワード | Big data / high utility patterns / spatial information / data mining |
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| 研究実績の概要 |
High utility itemset mining is an important knowledge discovery in data mining. Exicisting studies ignored the spatial information of the items in the database and tried to find hidden patterns. We have observed that ignoring the spatial information results either in missing useful information or generating suprious information that is not useful to the experts. In this year, we have tested our cliam, which is important to capture the spatial information of the items to find patterns that have high value. The work was published in Applied Intelligence Journal.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
We were successful in downloading the air pollution data of various sensors in Japan, USA, and other countries. We were able to test the distribution of characteritics of the data. We found that existing high utility itemset mining algorithms were inadequate to find spatially interesting patterns as they ignored the spatial information. We proposed a new model and an efficient algorithm to discover spatially interesting patterns having high value. Our work also published in Applied Intelligence journal.
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| 今後の研究の推進方策 |
1. This year we will model the air pollution has uncertain data generated by the sensors and study the current limitations. Next, we will analyze the results and make claim, followed by introducing a new model and algorithm to find spatially interesting patterns in uncertain data.
2. We will also study how to model air pollution as fuzzy dataset to extract useful information
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| 次年度使用額が生じた理由 |
We will write necessary programs (ETL-techniques) to analyze air pollution data and make the decision on importance of model the air pollution data as uncertain data and/or fuzzy data.We will develop models to capture the hidden information uncertain/fuzzy data. We will investigate algorithms to extract information.
This year we will using the Kakenhi grant on development of ETL-techniques (Miscellaneous) and article processing fees.
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