The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Experiments are carried out on datasets with different dimensions selected from UCI datasets by using two classical clustering algorithms. The results of the experiments indicate that when the dimensionality of the real dataset is less than or equal to 30, the clustering algorithms based on distance are effective. For high-dimensional datasets--dimensionality is greater than 30, the clustering algorithms...
With data mining becoming more and more important in many research area, cluster analysis has played an important role in geoscience application domain. Though the range of clustering algorithms that have been developed is broad, this paper will classify them according the broad approach or method adopted by each: a partitioning method, a hierarchical method, a density-based method, a grid-based method,...
Due to the complexity of geoscientific data, such as geochemical data, geophysical data and digital remote sensing data, traditional data mining methods, such as cluster analysis and association analysis, have limitations in resources evaluation. In this paper, a clustering algorithm is presented which has the ability to handle clusters of arbitrary shapes, sizes and densities. For association analysis,...
For applications of data mining techniques in geosciences, through mining spatial databases which are constructed with geophysical and geochemical data measured in fields, the knowledge, such as the spatial distribution of geological targets, the geophysical and geochemical characteristics of geological targets, the differentiation among the geological targets, and the relationship among geophysical...
In association analysis, mining the continuous attributes may reveal useful and interesting insights about the data objects which are of continuous attributes. Quantitative association rules are aimed to deal with the relationships among continuous attributes of data objects. This paper presents an association analysis algorithm based on the distances among clusters. The algorithm uses a clustering...
For very large databases, such as spatial database and multimedia database, the traditional clustering algorithms are of limitations in validity and scalability. According to the notion of clustering feature of BIRCH, an incremental clustering algorithm is designed and implemented, which solves the problems of effectiveness, space and time complexities of clustering algorithms for very large spatial...
For applications of clustering algorithms, the key techniques are to handle complicatedly distributed clusters and process massive data effectively and efficiently. On the basis of analysis and research of traditional clustering algorithms, a clustering algorithm based on density and adaptive density-reachable is presented in this paper, which can handle clusters of arbitrary shapes, sizes and densities...
Three clustering methods are presented and discussed by experimental analysis. The results by using three clustering methods which are partitioning methods, hierarchical methods and density-based methods visually illustrate the clustering results, in two-dimensional data sets as experimental data are used. Clearly, when the original data set is spherical shape, most of the cluster methods can get...
The traditional clustering algorithms are only suitable for the static datasets. As for the dynamic and incremental datasets, the clustering results will become unreliable after data updates, and also it will certainly decrease efficiency and waste computing resources to cluster all of the data again. To overcome these problems, a new incremental clustering algorithm is proposed on the basis of density...
Digital Ecosystem is defined as ldquoan open, loosely coupled, domain clustered, demand-driven, self-organizing and agent-based environment, in which each species is proactive and responsive for its own benefit and profitrdquo [1]. Species in the Digital Ecosystem can play dual roles, which are service requester (client) service provider (server). A service provider enters the Digital Ecosystem by...
For applications of clustering algorithms, a key technique is to handle complicatedly distributed clusters effectively and efficiently. On the basis of analysis and research of traditional clustering algorithms, a clustering algorithm based on density and adaptive density-reachable is presented in this paper. Experimental results show that the algorithm can handle clusters of arbitrary shapes, sizes...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.