Papers by Norashikin Ahmad
Self-organizing map (SOM) provides both clustering and visualization capabilities in mining data.... more Self-organizing map (SOM) provides both clustering and visualization capabilities in mining data. Dynamic self-organizing maps such as Growing Self-organizing Map (GSOM) has been developed to overcome the problem of fixed structure in SOM to enable better representation of the discovered patterns. However, in mining large datasets or historical data the hierarchical structure of the data is also useful to view the cluster formation at different levels of abstraction. In this paper, we present a technique to generate concept trees from the GSOM. The formation of tree from different spread factor values of GSOM is also investigated and the quality of the trees analyzed. The results show that concept trees can be generated from GSOM, thus, eliminating the need for re-clustering of the data from scratch to obtain a hierarchical view of the data under study.
2008 4th International Conference on Information and Automation for Sustainability, 2008
Protein sequence analysis is an important task in bioinformatics. The classification of protein s... more Protein sequence analysis is an important task in bioinformatics. The classification of protein sequences into groups is beneficial for further analysis of the structures and roles of a particular group of protein in biological process. It also allows an unknown or newly found sequence to be identified by comparing it with protein groups that have already been studied. In this paper, we present the use of Growing Self-Organizing Map (GSOM), an extended version of the Self-Organizing Map (SOM) in classifying protein sequences. With its dynamic structure, GSOM facilitates the discovery of knowledge in a more natural way. This study focuses on two aspects; analysis of the effect of spread factor parameter in the GSOM to the node growth and the identification of grouping and subgrouping under different level of abstractions by using the spread factor.
Neural Computing and Applications, 2009
Growing self-organizing map (GSOM) has been introduced as an improvement to the self-organizing m... more Growing self-organizing map (GSOM) has been introduced as an improvement to the self-organizing map (SOM) algorithm in clustering and knowledge discovery. Unlike the traditional SOM, GSOM has a dynamic structure which allows nodes to grow reflecting the knowledge discovered from the input data as learning progresses. The spread factor parameter (SF) in GSOM can be utilized to control the spread of the map, thus giving an analyst a flexibility to examine the clusters at different granularities. Although GSOM has been applied in various areas and has been proven effective in knowledge discovery tasks, no comprehensive study has been done on the effect of the spread factor parameter value to the cluster formation and separation. Therefore, the aim of this paper is to investigate the effect of the spread factor value towards cluster separation in the GSOM. We used simple k-means algorithm as a method to identify clusters in the GSOM. By using Davies-Bouldin index, clusters formed by different values of spread factor are obtained and the resulting clusters are analyzed. In this work, we show that clusters can be more separated when the spread factor value is increased. Hierarchical clusters can then be constructed by mapping the GSOM clusters at different spread factor values.
Artificial learning models such as artificial neural networks have been used for discovering patt... more Artificial learning models such as artificial neural networks have been used for discovering patterns that are hidden in the data and to solve problems in many application areas. Despite the successful implementation of the models, artificial learning models differ from human learning in several key aspects. One significant property of human learning is that human learning is incremental where the acquisition of knowledge happens gradually and / or in stages and is a lifelong process. Some aspects of learning may also be the result of genome modification over generations. Nevertheless, many of the existing artificial models do not take into account these incremental and dynamic aspects of human learning. Traditional artificial neural network models consider a data set as static and once the learning is completed cannot acquire new learning and adapt. In many models, the structures are not flexible to adapt, thus limiting the models capability in working with changing data or dynamic...
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Papers by Norashikin Ahmad