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A stereo obstacle detection method based on images symmetrical move and cell partition is presented in this paper. This algorithm gets each cell's optimum offset by calculating the minimum non-similarity of cell, and merges Cell of Interests (COIs) to calculate obstacles in the most probably obstacle regions, and gives information of these obstacles. The paper introduces the perspective projection...
Supplier categorization is considered as a business approach to reduce the logistic costs and improve business performance. In this work we propose a data clustering algorithm for supplier categorization namely S-Canopy clustering. It is simply making use of canopy clustering to reduce the number of distance comparisons. Comparison analysis shows a feasibility to obtain better results for categorization...
Outlier detection has numerous useful applications such as detecting criminal activities in electronic commerce, terrorism prediction and exceptional cases in many areas. Privacy and security concerns, however, arise while performing mining for outliers on distributed data. In this paper, we present two privacy preserving distance-based outlier detection algorithms over vertically partitioned data,...
In many applications of document clustering, a document may include multiple topics and thus may relate to multiple categories at the same time. Most of the existing subspace clustering algorithms can only perform hard clustering on document collections. In this paper, a fuzzy algorithm named R-FPC is introduced for document clustering. The algorithm discovers soft partitions of a data set in the...
K-means algorithm is one of the most popular clustering algorithms. However, it is sensitive to initialized partition and the circular dataset. To attack this problem, this paper introduced an improved k-means algorithm based on multiple feature points. The algorithm selects a number of feature points as cluster centroids unlike the traditional algorithm which only uses one centroid. In addition,...
This paper describes an evolutionary clustering algorithm, which can partition a given dataset automatically into the optimal number of groups through one shot of optimization. The proposed method is based on an evolutionary computing technique known as the Bacterial Evolutionary Algorithm (BEA). The BEA draws inspiration from a biological phenomenon of microbial evolution. Unlike the conventional...
Wireless sensor and actor networks (WSANs) are a set of coexistence sensors and actors connected wirelessly to perform cooperatively sensing and interaction to physical environment. Nowadays, WSANs are used in many important application areas. Main concern in all networks is connectivity of the network nodes, in WSANs because of majority of their applications, connectivity issue is even more vital...
Spatial data mining (SDM) is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. Being an important role of SDM, spatial clustering is to organize a set of spatial objects into groups (or clusters) such that objects in the same group are similar to each other and different from those in other groups. Spatial clustering has been...
The multidepot vehicle routing problem with interdepot routes (MDVRPI) is an extension to the classical vehicle routing problem (VRP); it is the major research topics in the supply chain management field. It is an extension of the multidepot vehicle routing problem in which vehicles may be replenished at intermediate depots along their route. In this paper, we propose a heuristic combining the adaptative...
In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the k-means and spectral clustering algorithms as alternatives to the commonly used seed-based analysis. To enable clustering of the entire brain volume, we use the Nystrom Method to approximate the necessary spectral decompositions. We apply k-means, spectral...
Outlier detection is the process of detecting the data objects which are grossly different from or inconsistent with the remaining set of data. Some of the important applications in the field of data mining are fraud detection, customer behavior analysis, and intrusion detection. There are number of good research algorithms for detecting outliers if the entire data is available and algorithms can...
Clustering techniques have been used by many intelligent software agents in order to retrieve, filter, and categorize documents available on the World Wide Web. Clustering is also useful in extracting salient features of related Web documents to automatically formulate queries and search for other similar documents on the Web. Traditional clustering algorithms either use a priori knowledge of document...
Wireless sensor and actor networks (WSANs) consist of powerful actors and resource constraint sensors, which are linked together in wireless networks. In some applications, actors must communicate with each other to make appropriate decisions and perform coordinated actions. Actor-actor network connectivity is vital to networks in such applications. Since WSAN applications are mostly deployed in harsh...
Clustering is an important task in spatial data mining and spatial analysis. We propose a clustering algorithm P-DBSCAN to cluster polygons in space. P-DBSCAN is based on the well established density-based clustering algorithm DBSCAN. In order to cluster polygons, we incorporate their topological and spatial properties in the process of clustering by using a distance function customized for the polygon...
This paper focuses on clustering algorithm of many-dimensional objects, where only the distances between objects are used. Centers of classes are found with the aid of neuron-like procedure with lateral inhibition. The result of clustering does not depend on starting conditions. Our algorithm makes it possible to give an clusters that really exist in the empirical data.
This paper analyses the advantages and disadvantages of the K-means algorithm and the DENCLUE algorithm. In order to realise the automation of clustering analysis and eliminate human factors, both partitioning and density-based methods were adopted, resulting in a new algorithm - Clustering Algorithm based on object Density and Direction (CADD). This paper discusses the theory and algorithm design...
This paper presents a comprehensive analysis of a novel temporal dataset of Shewanella oneidensis. Here we propose to cluster the temporal gene expression data of Shewanella oneidensis to define its molecular response at different time intervals following acidic pH and basic pH exposure and to find a relation of these temporal data at different environmental conditions. A mapping between those clusters...
An outlier is the object which is very different from the rest of the dataset on some measure. Finding such exception has received much attention in the data mining field. In this paper, we propose a KNN based outlier detection algorithm which is consisted of two phases. Firstly, it partitions the dataset into several clusters and then in each cluster, it calculates the Kth nearest neighborhood for...
Traditional clustering approaches usually analyze static datasets in which objects are kept unchanged after being processed, but many practical datasets are dynamically modified which means some previously learned patterns have to be updated accordingly. Re-clustering the whole dataset from scratch is not a good choice due to the frequent data modifications and the limited out-of-service time, so...
The CLARA algorithm is one of the popular clustering algorithms in use nowadays. This algorithm works on a randomly selected subset of the original data and produces near accurate results at a faster rate than other clustering algorithms. CLARA is basically used in data mining applications. We have used this algorithm for color image segmentation.The original CLARA is modified for producing better...
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