SOM Algorithms Research Articles

A directed batch growing approach to enhance the topology preservation of self-organizing map

The growing self-organizing map (GSOM) possesses effective capability to generate feature maps and visualizing high-dimensional data without pre-determining their size. Most of the proposed growing SOM algorithms use an incremental learning strategy. The conventional growing approach of GSOM is based on filling all available position around the candidate neuron which can decrease the topology preservation quality of the map due to the misconfiguration and twisting of the map which could be a consequence of unexpected network growth and improper neuron addition and weight initialization. To overcome this problem, in this paper we introduce a batch learning strategy for growing self-organizing maps called DBGSOM which direct the growing process based on the accumulative error around the candidate boundary neuron. In the proposed growing approach, just one new neuron is added around each candidate boundary neuron. The DBGSOM offers suitable mechanisms to find a proper growing positions and allocating initial weight vectors for the new neurons.The potential of the DBGSOM was investigated with one synthetic dataset and six real-world benchmark datasets in terms of topology preservation and mapping quality. Experimental results showed that the proposed growing strategy provides an enhanced topology preserved map and reduces the susceptibility of twisting compared to GSOM. Furthermore, the proposed method has a better clustering ability than GSOM and SOM. According to the lower number of neurons generated by DBGSOM, it needs less time to learn the manifold of the data points compared to GSOM.

Analysis of Dravidian words uttered by deaf speakers using clustering techniques: deaf speech enhancement

A novel method for grouping the fundamental speech features of normal and deaf speakers using a new Modified Self Organising Map (M-SOM) clustering algorithm, and deaf speech enhancement using distance measure is presented. The M-SOM algorithm will automatically categorise the normal and deaf speaker speech features into two different clusters. Its performance is analysed in comparison with SOM and NMTF algorithms. The result obtained reveals that the M-SOM algorithm which is developed by combining the adaptive features of SOM and the Ward clustering methods provides lowest intra and highest inter-clustering and appears to be the best method for deaf speech signals. After clustering estimation of distance metric, termed as correction measure facilitates enhancement of the unclear deaf speech so that it could be understandable to normal speakers. Hence it can be inferred that the suggested method effectively enhances the deaf speech signal for better understanding by a normal listener.

Analysis of Dravidian words uttered by deaf speakers using clustering techniques: deaf speech enhancement

A novel method for grouping the fundamental speech features of normal and deaf speakers using a new Modified Self Organising Map (M-SOM) clustering algorithm, and deaf speech enhancement using distance measure is presented. The M-SOM algorithm will automatically categorise the normal and deaf speaker speech features into two different clusters. Its performance is analysed in comparison with SOM and NMTF algorithms. The result obtained reveals that the M-SOM algorithm which is developed by combining the adaptive features of SOM and the Ward clustering methods provides lowest intra and highest inter-clustering and appears to be the best method for deaf speech signals. After clustering estimation of distance metric, termed as correction measure facilitates enhancement of the unclear deaf speech so that it could be understandable to normal speakers. Hence it can be inferred that the suggested method effectively enhances the deaf speech signal for better understanding by a normal listener.

A novel clustering approach for short-term solar radiation forecasting

This paper proposes a hybrid solar radiation forecasting method based on a novel game theoretic self-organizing map (GTSOM). New strategies are proposed to resolve the limitations of the original SOM for non-winning neurons and increase their competition with winning neurons to obtain more input patterns. Neural gas (NG) and competitive Hebbian Learning (CHL) are used to enhance the learning and quality of the map. Solar radiation data are decomposed by the discrete wavelet transform (DWT). A time series analysis is then used to develop the structure of the training and testing for Bayesian neural networks (BNNs). The proposed GTSOM groups the time-series analyzed datasets into clusters with similar data. The elbow method is used to determine the number of clusters. A cluster selection method is developed to determine the appropriate cluster whose solar radiation data provide the input to the NN. Temperature, wind speed and wind direction data are also included in the inputs to the BNN whose outputs provide the solar radiation forecasts. The historical solar radiation data are used to evaluate the accuracy of the hybrid forecasting with the proposed clustering and its comparison with that of the K-means, the original SOM and NG clustering algorithms. The comparison demonstrates the superior performance of the proposed clustering method.

Frame Work for Semi-Supervised Clustering based on Color Constraints to Enhance Text Mining for Efficient Information Retrieval

Background/Objectives: In this paper we have analyzed various issues with clustering and text mining. The collected documents are preprocessed and grouped using our proposed new algorithm based on position method. We proved our proposed color based constraint clustering algorithm out performs than K-Means and SOM algorithms in terms of time and reliability factors. Methods/Statistical Analysis: We identified the problem after analyzing the existing works with the help of articles from reputed journal papers and national and International level conferences. We proposed the new methodology for document grouping process, and color based constraint clustering process. Clustering can be considered as the most important semi-supervised learning problem which deals with finding a structure in a collection of unlabelled data. In this work the collected documents are preprocessed by stop word removal and stemming process and then the words are grouped according to their similarity using color code constraints. Performances of SOM and Kmeans, and color based constraint algorithms are analyzed for any kind of text document collections. Findings: In this work our proposed color based constraint (CBC) algorithm, SOM and K-Means algorithms performances are compared against time based frequency and reliability of retrieved documents. Here, the time needed to process the number of documents is analyzed. Reliability of retrieved documents can be made by using the number documents and the frequency measurement. We proved our proposed color based constraint clustering algorithm out performs than K-Means, and SOM algorithms in terms of time and reliability. Application/Improvements: Our work is useful for efficient information retrieval process. In future this work can be extended to maximize the grouping of words with minimum latency and one can also extend this work to develop an algorithm for maximize the grouping(clustering) of words in a document with color based constraints to increase the clustering performance for efficient text mining. Keywords: Color Based Constraint, Clustering, Information Retrieval, Semi_Supervised Clustering Technique, Text Mining

A CLUSTER ANALYSIS AND DECISION TREE HYBRID APPROACH IN DATA MINING TO DESCRIBING TAX AUDIT

In this research, we use clustering and classification methods to mine the data of tax and extract the information about tax audit by using hybrid algorithms K-MEANS, SOM and HAC algorithms from clustering and CHAID and C4.5 algorithms from decision tree and it produce the better results than the traditional algorithms and compare it by applying on tax dataset. Clustering method will use for make the clusters of similar groups to extract the easily features or properties and decision tree method will use for choose to decide the optimal decision to extract the valuable information from samples of tax datasets? This comparison is able to find clusters in large high dimensional spaces efficiently. It is suitable for clustering in the full dimensional space as well as in subspaces. Experiments on both synthetic data and real-life data show that the technique is effective and also scales well for large high dimensional datasets

Research on Fault Diagnosis Based on BP-SOM Hybrid Algorithms for NC Machine

NC machines are more and more widely used in manufacturing industry. As the key equipments in the factory, NC machines should be closely monitored to avoid faults. Therefore, the research on multi-function diagnosis based on BP-SOM is discussed for NC machine in the paper. Taking the precision data as the basic data for diagnosis, the research used BP and SOM algorithms to build up diagnosis models. As a result, the research has accomplished the diagnosing process and acquires good simulation output, which resembles with the real fact.

Clustering Molecular Dynamics Trajectories: 1. Characterizing the Performance of Different Clustering Algorithms.

Molecular dynamics simulation methods produce trajectories of atomic positions (and optionally velocities and energies) as a function of time and provide a representation of the sampling of a given molecule's energetically accessible conformational ensemble. As simulations on the 10-100 ns time scale become routine, with sampled configurations stored on the picosecond time scale, such trajectories contain large amounts of data. Data-mining techniques, like clustering, provide one means to group and make sense of the information in the trajectory. In this work, several clustering algorithms were implemented, compared, and utilized to understand MD trajectory data. The development of the algorithms into a freely available C code library, and their application to a simple test example of random (or systematically placed) points in a 2D plane (where the pairwise metric is the distance between points) provide a means to understand the relative performance. Eleven different clustering algorithms were developed, ranging from top-down splitting (hierarchical) and bottom-up aggregating (including single-linkage edge joining, centroid-linkage, average-linkage, complete-linkage, centripetal, and centripetal-complete) to various refinement (means, Bayesian, and self-organizing maps) and tree (COBWEB) algorithms. Systematic testing in the context of MD simulation of various DNA systems (including DNA single strands and the interaction of a minor groove binding drug DB226 with a DNA hairpin) allows a more direct assessment of the relative merits of the distinct clustering algorithms. Additionally, means to assess the relative performance and differences between the algorithms, to dynamically select the initial cluster count, and to achieve faster data mining by "sieved clustering" were evaluated. Overall, it was found that there is no one perfect "one size fits all" algorithm for clustering MD trajectories and that the results strongly depend on the choice of atoms for the pairwise comparison. Some algorithms tend to produce homogeneously sized clusters, whereas others have a tendency to produce singleton clusters. Issues related to the choice of a pairwise metric, clustering metrics, which atom selection is used for the comparison, and about the relative performance are discussed. Overall, the best performance was observed with the average-linkage, means, and SOM algorithms. If the cluster count is not known in advance, the hierarchical or average-linkage clustering algorithms are recommended. Although these algorithms perform well, it is important to be aware of the limitations or weaknesses of each algorithm, specifically the high sensitivity to outliers with hierarchical, the tendency to generate homogenously sized clusters with means, and the tendency to produce small or singleton clusters with average-linkage.