Automatic Patent Clustering using SOM and Bibliographic Coupling

Clustering trajectories using SOM

Nowadays data acquisition methods continuously record all sort of events occurring in the physical world. Improvements on both hardware and software technologies allow us to collect huge amounts of data, producing, every day, more complete, accurate and detailed pictures of human activity and interaction with the environment. General purpose tools are required for motion patterns, detection of ...

Spatial Clustering Using Hierarchical SOM

Identifying missing relevant patent citation links by using bibliographic coupling in LED illuminating technology

This study uses bibliographic coupling to identify missing relevant patent links, in order to construct a comprehensive citation network. Missing citation links can be added by taking the missing relevant patent links into account. The Pareto principle is used to determine the threshold of bibliographic coupling strength, in order to identify the missing relevant patent links. Comparisons betwe...

Unsupervised Clustering of Texture Features Using SOM and Fourier Transform

Texture analysis has a wide range of real-world applications. This paper presents a novel technique for texture feature extraction and compares its performance with a number of other existing techniques using a benchmark image database. The proposed feature extraction technique uses 2 D D R transform and self-organizing map (SOM). A combination of 2D-DFT and SOM with optimal parameter settings ...

Embedded Classification Kernel Using Som Clustering and Mixture of Experts

-In this paper, we introduce a new classification kernel by embedding self organized map (SOM) clustering with mixture of radial basis function (RBF) networks. The model’s efficacy is demonstrated in solving a multi-class TIMIT speech recognition problem where the kernel is used to learn the multidimensional cepstral feature vectors to estimate their posterior class probabilities. The tests res...