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Given a spatial network and a collection of activity events (e.g., emergency requests, crime reports, accident reports, etc.), spatial network activity summarization (SNAS) finds a set of k shortest paths based on the activity events. SNAS is important for critical societal applications such as disaster response and crime analysis. SNAS is computationally challenging because of the potentially exponential...
In this paper we propose is an extension of kernel k-means clustering algorithm for symbolic interval data with aggregated kernel functions. To evaluate this method, experiments with synthetic interval data set was performed and we have been compared our method with a dynamic clustering algorithm with single adaptive distance. The evaluation is based on an external cluster validity index (corrected...
Fuzzy c-means algorithm (FCM) based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. In this paper, an improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance by taking a new threshold value and a new convergent process is proposed. The experimental results of three real data sets...
Document clustering is related to data clustering concept which is one of data mining tasks and unsupervised classification. It is often applied to the huge data in order to make a partition based on their similarity. Initially, it used for Information Retrieval in order to improve the precision and recall from query. It is very easy to cluster with small data attributes which contains of important...
This paper proposes an additional version of the fuzzy c-means based classifier (FCMC). The classifier FCMC-R treats relational data instead of object data. FCMCs use covariance structures to represent flexible shapes of clusters. Despite its effectiveness, the intense computation of covariance matrices is an impediment for classifying a set of high-dimensional feature data. In order to tackle with...
The cluster analysis deals with the problems of organization of a collection of data objects into clusters based on similarity. It is also known as the unsupervised classification of objects and has found many applications in different areas. An important component of a clustering algorithm is the distance measure which is used to find the similarity between data objects. K-means is one of the most...
A major use of microarray data is to classify genes with similar expression profiles into groups in order to investigate their biological significance. Cluster analysis is by far the most used technique for gene expression analysis. It has grown to be an important research topic in a wide variety of fields owing to its wide applications. A number of clustering methods exist with one or more limitations,...
Distance computation is one of the most computationally intensive operations employed by many data mining algorithms. Performing such matrix computations within a DBMS creates many optimization challenges. We propose techniques to efficiently compute Euclidean distance using SQL queries and user-defined functions (UDFs). We concentrate on efficient Euclidean distance computation for the well-known...
Rival penalized competitive learning (RPCL) and its variants can perform clustering analysis efficiently with the ability of selecting the cluster number automatically. Although they have been widely applied in a variety of research areas, some of their problems have not yet been solved. Based on the semi-competitive learning mechanism of competitive and cooperative learning (CCL), this paper presents...
This paper describes a speaker-independent accent-based natural language call-routing system. Based on a speaker's accent group, this system directs customer calls to the automatic speech recognition system that is most suitable to recognize the input query. The speech recognition system understands the caller's query and converts it into routing keywords. Accent identification is the most important...
Neural networks are known for their ability to learn and classify patterns based on certain criteria defined within the training process. Fuzzy ARTMAP neural networks are examples of such systems where the output is decided based on the input/output pattern training scheme. In this research, we build a fuzzy ARTMAP like neural network that depends on an adaptive Euclidian distance neighborhood rather...
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