지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.20 No.2
- 발행연도
- 2020.6
- 수록면
- 156 - 167 (12page)
- DOI
- 10.5391/IJFIS.2020.20.2.156
이용수
초록· 키워드
오류제보하기
Semi-supervised clustering relies on both labeled and unlabeled data to steer the clustering process towards optimal categorization and escape from local minima. In this paper, we propose a novel fuzzy relational semi-supervised clustering algorithm based on an adaptive local distance measure (SSRF-CA). The proposed clustering algorithm utilizes side-information and formulates it as a set of constraints to supervise the learning task. These constraints are expressed using reward and penalty terms, which are integrated into a novel objective function. In particular, we formulate the clustering task as an optimization problem through the minimization of the proposed objective function. Solving this optimization problem provides the optimal values of different objective function parameters and yields the proposed semi-supervised clustering algorithm. Along with its ability to perform data clustering and learn the underlying dissimilarity measure between the data instances, our algorithm determines the optimal number of clusters in an unsupervised manner. Moreover, the proposed SSRF-CA is designed to handle relational data. This makes it appropriate for applications where only pairwise similarity (or dissimilarity) information between data instances is available. In this paper, we proved the ability of the proposed algorithm to learn the appropriate local distance measures and the optimal number of clusters while partitioning the data using various synthetic and real-world benchmark datasets that contain varying numbers of clusters with diverse shapes. The experimental results revealed that the proposed SSRF-CA accomplished the best performance among other state-of-the-art algorithms and confirmed the outperformance of our clustering approach.
목차
Abstract
1. Introduction
2. Related Works
3. Proposed Approach
4. Experimental Results
5. Conclusions
References
참고문헌 (23)
참고문헌 신청
R. Greenlaw / 2013 / Survey of clustering : algorithms and applications / International Journal of Information Retrieval Research 3(2):1~29
S. Wazarkar / 2018 / A survey on image data analysis through clustering techniques for real world applications / Journal of Visual Communication and Image Representation 55:596~626
D. Xu / 2015 / A comprehensive survey of clustering algorithms / Annals of Data Science 2(2):165~193
Z. Yu / 2017 / Adaptive ensembling of semisupervised clustering solutions / IEEE Transactions on Knowledge and Data Engineering 29(8):1577~1590
S. Saha / 2016 / Brain image segmentation using semi-supervised clustering / Expert Systems with Applications 52:50~63
S. Wazarkar / 2018 / A survey on image data analysis through clustering techniques for real world applications / Journal of Visual Communication and Image Representation 55:596~626
D. Xu / 2015 / A comprehensive survey of clustering algorithms / Annals of Data Science 2(2):165~193
Z. Yu / 2017 / Adaptive ensembling of semisupervised clustering solutions / IEEE Transactions on Knowledge and Data Engineering 29(8):1577~1590
S. Saha / 2016 / Brain image segmentation using semi-supervised clustering / Expert Systems with Applications 52:50~63
함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Synthesis of clustering algorithms based on selection of centroids
제어로봇시스템학회 국제학술대회 논문집
2018 .10
-
합성곱 오토인코더 기반의 응집형 계층적 군집 분석
멀티미디어학회논문지
2020 .01
-
The Effect of Bias in Data Set for Conceptual Clustering Algorithms
International journal of advanced smart convergence
2019 .01
-
Spectral clustering based on the local similarity measure of shared neighbors
[ETRI] ETRI Journal
2022 .10
-
이상치를 고려한 딥러닝 기반 웨이퍼 맵 결함 패턴 클러스터링
신뢰성응용연구
2022 .12
-
Performance evaluation of principal component analysis for clustering problems
Journal of Advanced Marine Engineering and Technology (JAMET)
2016 .10
-
Temporal Clustering with Self-Supervised Image Networks
한국통신학회 인공지능 학술대회 논문집
2024 .09
-
클러스터 밀도와 무관한 향상된 클러스터링 기법
한국컴퓨터정보학회논문지
2015 .12
-
상황인식 기반 클러스터링의 적응적 자율 학습 분할 알고리즘
한국정보통신학회 종합학술대회 논문집
2022 .05
-
상황인식 기반 클러스터링의 적응적 자율 학습 분할 알고리즘
한국정보통신학회 종합학술대회 논문집
2022 .10
-
딥러닝을 이용한 시계열데이터 군집화
신뢰성응용연구
2019 .06
-
Fine-Grained Mobile Application Clustering Model Using Retrofitted Document Embedding
[ETRI] ETRI Journal
2017 .08
-
상황인식 기반 클러스터링의 비지도 학습
한국정보통신학회 종합학술대회 논문집
2020 .07
-
A Mixed Co-clustering Algorithm Based on Information Bottleneck
JIPS(Journal of Information Processing Systems)
2017 .01
-
An Evolving Fuzzy Model to Determine an Optimal Number of Data Stream Clusters
INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS
2022 .09
-
A New Image Clustering Method Based on the Fuzzy Harmony Search Algorithm and Fourier Transform
JIPS(Journal of Information Processing Systems)
2016 .12
-
클러스터링 기법을 이용한 수용가별 전력 데이터 패턴 분석
KEPCO Journal on electric power and energy
2016 .01
-
재사용 배터리의 성능 편차를 고려한 DBSCAN 및 PCA 기법 기반 클러스터링 알고리즘
전력전자학회논문지
2023 .08
-
Identification of Plastic Wastes by Using Fuzzy Radial Basis Function Neural Networks Classifier with Conditional Fuzzy C-Means Clustering
Journal of Electrical Engineering & Technology
2016 .11
이 논문의 저자 정보
이 논문과 함께 이용한 논문
Effectiveness of Mass Drug Administration on Neglected Tropical Diseases in Schoolchildren in Zanzibar, Tanzania
Ju Yeong Kim
,
Seobo Sim
,
Eun Joo Chung
외 7명
Parasites, Hosts and Diseases
2020
.04
지속가능발전의 의미와 미래생활과학 분야로의 연계성
이소라
한국지역사회생활과학회 학술대회 자료집
2019
.04
식품산업 현황과 R&D 미래 대응전략: 공공부문의 역할과 추진방향을 중심으로
전지영
,
함상욱
,
박진성
외 2명
식품과학과 산업
2020
.06
해양경계 모호성의 딜레마 : 동북아 방공식별구역의 군사적 충돌과 해양신뢰구축조치
반길주
아세아연구
2020
.06
From Nano in Vacuum to Bio in Ambient
문대원
진공이야기
2020
.06
최근 본 자료
댓글(0)
0