The proposed IKMN+ algorithm, a modification of the incrementalKMN uses this best distance measure to obtain a partition-based clustering. Our findings revealed that IKNM+ could overcome the issue of initial centroid selection ofk-means algorithm and provides good performance in clustering several ...
In this paper, three partition-based algorithms, PAM, CLARA and CLARANs are combined with k-medoid distance based outlier detection to improve the outlier detection and removal process. The experimental results prove that CLARANS clustering algorithm when combined with medoid distance based outlier ...
To solve the problem, this paper presents a spatial distance-based spatial clustering algorithm for sparse image data (SDBSCA-SID). Firstly, the imaging range of the image sensor constitutes a two-dimensional (2D) constraint space. Under the constraint, spatial clustering was carried out based ...
K-means algorithm dependence on partition-based clustering technique is popular and widely used and applied to a variety of domains. K-means clustering results are extremely sensitive to the initial centroid; this is one of the major drawbacks of k-means algorithm. Due to such sensitivity; ...
In this article, we consider distance-based clustering problems. In contrast to many approaches, we use the maximum norm instead of the more commonly used
14.1.4.1 K-Means Clustering In the K-means clustering algorithm, which is a hard-clustering algorithm, we partition the dataset points into K clusters based on their pairwise distances. We typically use the Euclidean distance, defined by Eq. (14.2), that is, for two data points xi = (xi...
例如Osyczka and Dundu[1 7] 提出 ㆒种配合距离度量(distance metric) 的参考值的 Pareto 评估函数, 以供选择运算使用 … www.docin.com|基于2个网页 3. 距离尺度 查询种类及... ... ◆基于空间划分( Region partition based) ●距离尺度(distance metric) ◆基于预处理( Based on Precompu… ...
We propose a sequence clustering algorithm and compare the partition quality and execution time of the proposed algorithm with those of a popular existing algorithm. The proposed clustering algorithm uses a grammar-based distance metric to determine partitioning for a set of biological sequences. The ...
Huang H, Cheng Y, Zhao R (2008) A semi-supervised clustering algorithm based on must-link set. In: Proceedings of the international conference on advanced data mining and applications, pp 492–499 Hubert L, Arabie P (1985) Comparing partitions. J Classif 2(1):193–218 Article MATH Google...
14.1.4.1 K-Means Clustering In the K-means clustering algorithm, which is a hard-clustering algorithm, we partition the dataset points into K clusters based on their pairwise distances. We typically use the Euclidean distance, defined by Eq. (14.2), that is, for two data points xi = (xi...