{"_buckets": {"deposit": "3a59d403-2fdb-4449-b34f-503c35828247"}, "_deposit": {"id": "18615", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "18615"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00018615"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2014-07", "bibliographicIssueDateType": "Issued"}, "bibliographicPageEnd": "993", "bibliographicPageStart": "988", "bibliographic_titles": [{"bibliographic_title": "2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "In the field of marketing, a questionnaire is one of the most important approaches in order to research the market or to design a marketing strategy. On the other hand, people have a variety of individuality recently, then respondents have various impressions on evaluation objects. In the analysis of collected questionnaire data, it is important not only to analyze overall trends but also to discover minority groups which have strong impressions but are different from general groups. It is, however, difficult to extract minority groups by conventional cluster analysis applied to questionnaire data, because they generally aim at extracting majority groups or making a rough clustering. In this paper, we propose the extraction method of minority groups in questionnaire data using the spectral clustering method which considers local similarity and extracts the clusters having less connection to general groups.", "subitem_description_type": "Abstract"}]}, "item_10_description_5": {"attribute_name": "\u5185\u5bb9\u8a18\u8ff0", "attribute_value_mlt": [{"subitem_description": "2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2014), July 6-11, 2014, Beijing International Convention Center, Beijing, China (WCCI 2014)", "subitem_description_type": "Other"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "DOI", "subitem_identifier_uri": "http://dx.doi.org/10.1109/FUZZ-IEEE.2014.6891730"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/20713"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "IEEE"}]}, "item_10_relation_8": {"attribute_name": "ISBN", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "978-1-4799-2073-0", "subitem_relation_type_select": "ISBN"}}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "\u00a9 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "author"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Inagaki, Kazuto"}], "nameIdentifiers": [{"nameIdentifier": "54116", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Yoshikawa, Tomohiro"}], "nameIdentifiers": [{"nameIdentifier": "54117", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Furuhashi, Takeshi"}], "nameIdentifiers": [{"nameIdentifier": "54118", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-21"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "9_A_Study_on_Extraction_of_Minority_Groups.pdf", "filesize": [{"value": "502.1 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 502100.0, "url": {"label": "9_A_Study_on_Extraction_of_Minority_Groups.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/18615/files/9_A_Study_on_Extraction_of_Minority_Groups.pdf"}, "version_id": "9bb7ee7b-57fc-4ec1-bb98-300318906b03"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "A Study on Extraction of Minority Groups in Questionnaire Data based on Spectral Clustering", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "A Study on Extraction of Minority Groups in Questionnaire Data based on Spectral Clustering"}]}, "item_type_id": "10", "owner": "1", "path": ["320/502/503"], "permalink_uri": "http://hdl.handle.net/2237/20713", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2014-11-06"}, "publish_date": "2014-11-06", "publish_status": "0", "recid": "18615", "relation": {}, "relation_version_is_last": true, "title": ["A Study on Extraction of Minority Groups in Questionnaire Data based on Spectral Clustering"], "weko_shared_id": null}