12386699
Free parallel data mining
Data mining is computationally expensive. Since the benefits of data mining results are unpredictable, organizations may not be willing to buy new hardware for that purpose. We will present a system that enables data mining applications to run in parallel on networks of workstations in a fault-tolerant manner. We will describe our parallelization of a combinatorial pattern discovery algorithm and a classification tree algorithm. We will demonstrate the effectiveness of our system with two real applications: discovering active motifs in protein sequences and predicting foreign exchange rate movement.
32779102
A Framework for Biological Pattern Discovery on Networks of Workstations
37868959
A Method of Raster Data Mining Based on Multi Dimension Data Set
Spatial Data Mining can mine automatically or semi-automatically unknown, creditable, effective, integrative or schematic knowledge which can be understood from the increasingly complex spatial database and enhance the ability of interpreting data to generate useful knowledge. There are large amounts of data existing in the database of Government GIS. However, a great deal of the data is idle, which has caused a huge waste of data due to rarely effectively utilization in practice. It is very necessary to deal with the task of data mining based on Government GIS. In this paper, an example on land utilization and land cover in a certain region of Guizhou Province was presented to describe the course of spatial data mining. There into, a derived star-type model was used to organize the raster data to form multi dimension data set. Under these conditions, clustering method was utilized to carry out data mining aiming at the raster data. Based on corresponding analyses, users can find out which types of vegetation were suitable for being cultivated in this region by using related knowledge in a macroscopic view. Therefore, feasible service information could be provided to promote economic development in the region.
43513684
A representation theorem for recovering contraction relations satisfying wci
A notion of an image structure associated with the canonical epistemic state is introduced. Based on it, we get a representation result for recovering contraction inference relations satisfying the condition weak conjunctive inclusion (wci) in terms of F-standard epistemic AGM states. In effect, this result establishes a representation theorem for belief contraction functions satisfying AGM postulates (k-1) - (k-7), and Rott's (wci) and (k-8c), and hence generalizes Rott's corresponding result in the finite framework.
13758087
Support cluster machine
For large-scale classification problems, the training samples can be clustered beforehand as a downsampling pre-process, and then only the obtained clusters are used for training. Motivated by such assumption, we proposed a classification algorithm, Support Cluster Machine (SCM), within the learning framework introduced by Vapnik. For the SCM, a compatible kernel is adopted such that a similarity measure can be handled not only between clusters in the training phase but also between a cluster and a vector in the testing phase. We also proved that the SCM is a general extension of the SVM with the RBF kernel. The experimental results confirm that the SCM is very effective for largescale classification problems due to significantly reduced computational costs for both training and testing and comparable classification accuracies. As a by-product, it provides a promising approach to dealing with privacy-preserving data mining problems.
5812894
Configuration Analysis for Reconfigurable Modular Planetary Robots Based on MSV and CSM
A reconfigurable modular planetary robot system (RMPRS) consists of the parent body and multiple asymmetric wheel-manipulator child-robot modules. The module, which can independently locomote and manipulate, possesses the orientation of posture and the direction of locomotion. The modules have reconfiguration capability so that a group of the modules can construct a variety of configurations. The aim of the robot reconfiguration is to generate better configuration with respect to the directional locomotion adapted to the environment. Module state vector (MSV) and configuration state matrix (CSM) are presented and constructed for representing the asymmetric module and the configurations, and supporting the transformation operation for triggering the elementary motions of the module and the reconfiguration. The algorithm for optimizing the assembly reconfiguration of discrete modules is proposed and the result is evaluated through numerical simulation in an example
15051068
Study on Epipolar Geometry Restoration of Parallel Binocular Vision
The epipolar geometry restoration offline and online for the parallel binocular vision system were discussed in this paper. For the former, a method that uses a common calibration board to realize automatic and accurate matching of the image corner pairs was developed; for the latter, a connotative constraint was proved by geometry theory and the corresponding cross-correlation sparse matching algorithm was introduced. The constraint was that for any space point projected into two images, the horizontal coordinate in the left image of this point is bigger than that in the right image. Standard and real images experiments were made for each kind of problem and the results show that the algorithms for each problem were valid.