Cluster examination is data mining task for the assignment of collection a set of items in such a path, to the point that questions in the same gathering (called a cluster) are more like one another than to those in different gatherings (clusters). K-means grouping is a technique for group investigation which intends to parcel n perceptions into k groups in which every perception fits in with the cluster with the closest mean. This paper, decided the aftereffect of standard parameter estimations of shading picture division with k-means and the modified k-means with ABC and ACO algorithms. The paper demonstrates that division of color picture with modified k-mean consolidated with swarm Intelligence calculations for color image segmentation gives preferable results over simple k-means and Modified k-means with Ant colony optimization gives better results than modified k-means with Artificial bee colony.
목차
Abstract 1. Introduction 2. Preliminaries 2.1. K-Means Clustering 2.2. Artificial Bee Colony 2.3. ANT Colony Optimization 3. Proposed Approach 3.1. Modified K-Means 3.2. ABCMK-Means 3.3. ACOMK-Means 4. Implementation and Results 4.1. Accuracy 4.2. Sensitivity 4.3. Specificity 4.4. F-Measure 4.5. Bit Error Rate 4.6. Execution Time 5. Conclusion and Future Work References
키워드
Data miningclusteringk-means algorithmswarm intelligenceartificial bee colonyant colony
저자
Kiranpreet [ M.tech CSE Dept., CT group of engg., mgmt.& tech., Asst.proff. M.tech CSE Dept.,CT group of engg.,mgmt.&tech. Jalandhar(India) ]
Prince Verma [ M.tech CSE Dept., CT group of engg., mgmt.& tech., Asst.proff. M.tech CSE Dept.,CT group of engg.,mgmt.&tech. Jalandhar(India) ]
보안공학연구지원센터(IJDTA) [Science & Engineering Research Support Center, Republic of Korea(IJDTA)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Database Theory and Application
간기
격월간
pISSN
2005-4270
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Database Theory and Application Vol.8 No.5