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Register a new userAn Efficient Preprocessing Methodology for Discovering Patterns and Clustering of Web Users using a Dynamic ART1 Neural Network
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Added by
Ramya C
Publication date
2011
fields
Informatics Engineering
and research's language is
English
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In this paper, a complete preprocessing methodology for discovering patterns in web usage mining process to improve the quality of data by reducing the quantity of data has been proposed. A dynamic ART1 neural network clustering algorithm to group users according to their Web access patterns with its neat architecture is also proposed. Several experiments are conducted and the results show the proposed methodology reduces the size of Web log files down to 73-82% of the initial size and the proposed ART1 algorithm is dynamic and learns relatively stable quality clusters.
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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 and SOM when there are fewer clusters. As the number of clusters increases, average inter-cluster distance of ART1 is low compared to K-Means and SOM which indicates the high quality of clusters formed by our approach.
Web log data is usually diverse and voluminous. This data must be assembled into a consistent, integrated and comprehensive view, in order to be used for pattern discovery. Without properly cleaning, transforming and structuring the data prior to the analysis, one cannot expect to find meaningful patterns. As in most data mining applications, data preprocessing involves removing and filtering redundant and irrelevant data, removing noise, transforming and resolving any inconsistencies. In this paper, a complete preprocessing methodology having merging, data cleaning, user/session identification and data formatting and summarization activities to improve the quality of data by reducing the quantity of data has been proposed. To validate the efficiency of the proposed preprocessing methodology, several experiments are conducted and the results show that the proposed methodology reduces the size of Web access log files down to 73-82% of the initial size and offers richer logs that are structured for further stages of Web Usage Mining (WUM). So preprocessing of raw data in this WUM process is the central theme of this paper.
Polychronous neural groups are effective structures for the recognition of precise spike-timing patterns but the detection method is an inefficient multi-stage brute force process that works off-line on pre-recorded simulation data. This work presents a new model of polychronous patterns that can capture precise sequences of spikes directly in the neural simulation. In this scheme, each neuron is assigned a randomized code that is used to tag the post-synaptic neurons whenever a spike is transmitted. This creates a polychronous code that preserves the order of pre-synaptic activity and can be registered in a hash table when the post-synaptic neuron spikes. A polychronous code is a sub-component of a polychronous group that will occur, along with others, when the group is active. We demonstrate the representational and pattern recognition ability of polychronous codes on a direction selective visual task involving moving bars that is typical of a computation performed by simple cells in the cortex. The computational efficiency of the proposed algorithm far exceeds existing polychronous group detection methods and is well suited for online detection.
Web log data is usually diverse and voluminous. This data must be assembled into a consistent, integrated and comprehensive view, in order to be used for pattern discovery. Without properly cleaning, transforming and structuring the data prior to the analysis, one cannot expect to find meaningful patterns. As in most data mining applications, data preprocessing involves removing and filtering redundant and irrelevant data, removing noise, transforming and resolving any inconsistencies. In this paper, a complete preprocessing methodology having merging, data cleaning, user/session identification and data formatting and summarization activities to improve the quality of data by reducing the quantity of data has been proposed. To validate the efficiency of the proposed preprocessing methodology, several experiments are conducted and the results show that the proposed methodology reduces the size of Web access log files down to 73-82% of the initial size and offers richer logs that are structured for further stages of Web Usage Mining (WUM). So preprocessing of raw data in this WUM process is the central theme of this paper.
This paper presents a novel neural network design that learns the heuristic for Large Neighborhood Search (LNS). LNS consists of a destroy operator and a repair operator that specify a way to carry out the neighborhood search to solve the Combinatorial Optimization problems. The proposed approach in this paper applies a Hierarchical Recurrent Graph Convolutional Network (HRGCN) as a LNS heuristic, namely Dynamic Partial Removal, with the advantage of adaptive destruction and the potential to search across a large scale, as well as the context-awareness in both spatial and temporal perspective. This model is generalized as an efficient heuristic approach to different combinatorial optimization problems, especially to the problems with relatively tight constraints. We apply this model to vehicle routing problem (VRP) in this paper as an example. The experimental results show that this approach outperforms the traditional LNS heuristics on the same problem as well. The source code is available at href{https://github.com/water-mirror/DPR}{https://github.com/water-mirror/DPR}.
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