This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds. The SO-Net models the spatial distribution of point cloud by building a Self-Organizing Map (SOM). Based on the SOM, SO-Net performs hierarchical feature extraction on individual points and SOM nodes, and ultimately represents the input point cloud by a single feature vector. The rece...

This project comprises designing and implementing a hybrid recommender system for web–pages which uses data from a social tagging system to recommend interesting items to users. For the initial implementation, the tagging data will come from del.icio.us, the oldest and largest public social bookmarking system. The system will cluster items using a self–organizing maps (SOM) network and will inc...

DNA microarray technologies together with rapidly increasing genomic sequence information is leading to an explosion in available gene expression data. Currently there is a great need for efficient methods to analyze and visualize these massive data sets. A self-organizing map (SOM) is an unsupervised neural network learning algorithm which has been successfully used for the analysis and organi...

In this paper, we propose ART1 neural network clustering algorithm to group users according to their Web access patterns. We compare the quality of clustering of our ART1 based clustering technique with that of the K-Means and SOM clustering algorithms in terms of inter-cluster and intra-cluster distances. The results show the average inter-cluster distance of ART1 is high compared to K-Means a...

Breast cancer is the second leading cause of cancer mortality in women. Mammography remains the best method for early detection of cancers of the breast, capable of detecting small lumps up to two years before they grow large enough to be palpable on physical examination. X-ray images of the breast must be carefully evaluated to identify early signs of cancerous growth. Segmenting, or partition...

This paper modified the mechanism of weight adjusting of the Self-Organizing Mapping network (SOM) for solving the problems of topology preserving and clarifying boundary of clustering graph for the clustering analysis. The modified SOM is named the Multiple Clustering Centers SOM (MCC-SOM). The MCC-SOM changed the competitive learning mechanism of “winner takes all” to allow the more one clust...

Overfitting is a well-known problem in the fields of symbolic and connectionist machine learning. It describes the deterioration of generalisation performance of a trained model. In this paper, we investigate the ability of a novel artificial neural network, bp-som, to avoid overfitting. bp-som is a hybrid neural network which combines a multi-layered feed-forward network (mfn) with Kohonen’s s...

Reverse engineering is an important process in CAD systems today. Yet several open problems lead to a bottleneck in the reverse engineering process. First, because the topology of the object to be reconstructed is unknown, point connectivity relations are undefined. Second, the fitted surface must satisfy global and local shape preservation criteria that are undefined explicitly. In reverse eng...

For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production techn...

This paper presents a method for Data Mining and Knowledge Discovery in Image Data. This method is based on the Self-Organizing Map (SOM) which is an unsupervised artificial neural network algorithm. The SOM possesses unique properties of clustering, classification, modelling and visualization and is used here as a Data Mining tool. This enables us to get informative yet simpler pictures of the...