1、image segmentation by clustering
2、High availability through clustering.
3、virtual memory data clustering
4、Improved KNN using clustering algorithm
5、Chinese search result clustering based on key noun phrase clustering
6、Improved workload balancing through clustering.
7、Robust distributed k-mediods clustering algorithm
8、The clustering was for two purpose: clustering the over segmented parts and determining the category of the new clustering parts.
9、Question: When will clustering support be added?
10、Text and search results framework (with clustering).
11、Text extraction algorithm based on binary clustering
12、Does the clustering feature use Shoal framework?
13、It will output a low quality clustering result if user set unsuitable parameters before clustering operation.
14、Mixed clustering - This is a mix of both horizontal and vertical clustering.
15、Vertical clustering is achieved by clustering the queue managers using WebSphere MQ clustering, which optimizes processing and provides the following advantages.
16、Based on the traditional fuzzy C-means clustering algorithm, a new fuzzy C-means clustering algorithm for interval data clustering is proposed.
17、A requirement for clustering generally requires stateless behavior.
18、Vertical clustering — All cluster members are on the same node.
19、These are applications that can exploit asymmetric clustering.
20、Basically there is fuzziness in the procedure of clustering.
21、Optimal algorithm of data streams clustering on sliding window model;
22、Moreover, Siwpas Enterprise Edition includes clustering and failover of stateful EJB and CDI beans with Tomcat native clustering.
23、The centralized clustering refers to clustering with scale and focus within the tolerable scope of scale economy.
24、The modified kernel clustering algorithm is faster than the classical algorithm in convergence and more accurate in clustering.
25、Clustering of relational databases for scalability and availability is a well established discipline, and so we will not spend time discussing techniques for clustering relational databases.
26、Data stream is characterized by infinite data and quick stream speed, so traditional clustering algorithm cannot be applied to data stream clustering directly.
27、In this paper, by introducing the multi-view idea into conventional single-view discriminant clustering algorithm, a novel multi-view discriminate clustering (MVDC) algorithm is proposed.
28、Analyze the parameters of the fuzzy clustering method, ensure the self-adaptive clustering of the generators by the fuzzy statistical index.
29、To overcome the shortcomings of the GCOD, a high-dimensional clustering algorithm for data mining, the paper proposes an intersected grid clustering algorithm based on density estimation (IGCOD).
30、Forecast method of unascertained clustering to predict mining induced surface subsidence is optimized.
