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This paper proposes a new framework for data association to solve the problem of SLAM. The proposed framework has specific relevance to range scanner based EKF-SLAM. The resulting data representation enables semantic reasoning on a spatial level which reduces the misassociation of closely spaced data from different spatial configurations through the use of convex polygons to represent data from similar...
K-mean algorithm requires total number cluster, k beforehand in order the algorithm operates correctly. This pre-requisite value is needed to ensure the algorithm works on the tested data. In this paper, a test-and-generate approach is applied to estimate total number present in a data. A hybrid Bees Algorithm and cluster validity index are used for this purpose. The modified Bees algorithm is used...
Data clustering has been applied in multiple fields such as machine learning, data mining, wireless sensor networks and pattern recognition. One of the most famous clustering approaches is K-means which effectively has been used in many clustering problems, but this algorithm has some problems such as local optimal convergence and initial point sensitivity. Artificial fishes swarm algorithm (AFSA)...
In the field of pattern recognition, combination of different classifiers is a common method to improve classification accuracy. Recently, tendency to improve the function of clustering methods, specifically partitional clustering methods, is being increased. Generally, hierarchical clustering is preferred to partitional clustering when the number of exact clusters is undetermined or when we are interested...
Classical validity indices are limited to clusters of specific geometrical shapes, and do not allow for discovering the natural cluster structure in data. Moving from the traditional coherent gene expression clustering to exploring the connectivity of gene expression patterns demands the use of more efficient validity indices. In this work, the application of a novel validity measure to gene expression...
In the framework of Fuzzy Cognitive maps theory, we propose a novel classify algorithm, which is totally different from the traditional classify algorithm. The novel classify algorithm has three main advantages: Firstly, the procedures of the proposed algorithm are more transparent and understandable, and the classify results have shown the relationship between attributes. Secondly, the predefined...
It is a trend for paradigms of nature-inspired computing to hybrid. Inspired by the principle of immune response in the immune system, a novel incremental data clustering algorithm called IRA was proposed in previous work. It obtains high quality clustering. However, the number of clusters obtained by IRA is more than the actual ones. Therefore, the clustering algorithm based on ant colony called...
Word sense induction is usually viewed as a cluster problem in natural language processing. The context of the target word is represented as a vector and the cluster algorithms such as k-means, EM are applied. Different from the traditional methods, we proposed a new way based on “one sense per collocation” assumption which is proposed by Yarwosky (1993). Each sentence which...
Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data". Clustering is the automated search for group of related observations in a data set. The K-Means method is one of the most commonly used clustering techniques for a variety of applications. This paper proposes a method for making the K-Means algorithm...
Data mining has become an important topic in effective analysis of gene expression data due to its wide application in the biomedical industry. Within a gene expression matrix there are usually several particular macroscopic phenotypes of samples. Selection of genes most relevant and informative for certain phenotypes is an important aspect in gene expression analysis. Currently most of the research...
Living in the modern technology dependent world, we heavily rely on electronically stored data and information, to come up with sound and timely decisions. Considering the entire information technology world, there exists an unimaginable volume of data which contains a lot of information which is relevant to various kinds of fields. But the problem emerges when we are interested to find out about...
An effective XML cluster method called neighbor center clustering algorithm (NCC) is presented in this paper, whose similarity is obtained through both structural and content information contained in XML files. Structural similarity is measured by the idea of Longest Common Subsequence, while content similarity is achieved using TF-IDF principles. It reduces computation complexity by avoiding direct...
GPU hardware and software has been evolving rapidly. CUDA versions 1.1 and higher started supporting atomic operations on device memory, and CUDA versions 1.2 and higher started supporting atomic operations on shared memory. This paper focuses on parallelizing applications involving reductions on GPUs. Prior to the availability of support for locking, these applications could only be parallelized...
In this paper, an improved ant colony optimization based approach for image edge detection is proposed. The algorithm use ant colony clustering approach to extract edge feature. The approach set the heuristics information function and the initial cluster, thus avoiding the search blindness which carried out by traditional ant colony algorithm. And a series of simulation experiments demonstrate the...
Speaker clustering is one of the important tasks in speech processing. Its goal is not to understand or analyse the spoken language, but to separate recordings from multiple speakers or to analyse the recordings and determine the number of speakers. While there are advanced models for speech recognition and generation, a simpler method might be sufficient for clustering of the speech data. In this...
We present an approach for grouping single-speaker speech segments into speaker-specific clusters. Our approach is based on applying the K-means clustering algorithm to a suitable discriminant subspace, where the euclidean distance reflect speaker differences. A core feature of our approach is approximating speaker-conditional statistics, that are not available, with single-speaker segments statistics,...
An algorithm is presented for clustering sequential data in which each unit is a collection of vectors. An example of such a type of data is speaker data in a speaker clustering problem. The algorithm first constructs affinity matrices between each pair of units, using a modified version of the Point Distribution algorithm which is initially developed for mining patterns between vector and item data...
This paper addresses the problem of cluster characterization by selecting a subset of the most relevant features for each cluster from a categorical dataset in an autonomous way. The proposed autonomous model is based on the Relational Topological Clustering (RTC) associated with a statistical test which allows to detect the most important variables in an automatic way without setting any parameters...
Constrained clustering has been developed to improve clustering methods through pair wise constraints. Although the constraints are enhancing the similarity relations between the items, the clustering is conducted in the static feature space. In this paper we embed the information about the constraints to a feature selection procedure, that adapts the feature space regarding the constraints. We propose...
Conventional k-means only considers pair wise similarity during cluster assignment, which aims to minimizing the distance of points to their nearest cluster centroids. In high dimensional space like document datasets, however, two points may be nearest neighbors without belonging to the same class. Thus pair wise similarity alone is often insufficient for class prediction in such space. To that end,...
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