Efficient Spatial Data mining using Integrated Genetic Algorithm and ACO
Spatial data plays a key role in numerous applications such as network traffic, distributed security applications such as banking, retailing, etc., The spatial data is essential mine, useful for decision making and the knowledge discovery of interesting facts from large amounts of data. Many private institutions, organizations collect the number of congestion on the network while packets of data are sent, the flow of data and the mobility of the same. In addition other databases provide the additional information about the client who has sent the data, the server who has to receive the data, total number of clients on the network, etc. These data contain a mine of useful information for the network traffic risk analysis. Initially study was conducted to identify and predict the number of nodes in the system; the nodes can either be a client or a server. It used a decision tree that studies from the traffic risk in a network. However, this method is only based on tabular data and does not exploit geo routing location. Using the data, combined to trend data relating to the network, the traffic flow, demand, load, etc., this work aims at deducing relevant risk models to help in network traffic safety task.
The existing work provided a pragmatic approach to multi-layer geo-data mining. The process behind was to prepare input data by joining each layer table using a given spatial criterion, then applying a standard method to build a decision tree. The existing work did not consider multi-relational data mining domain. The quality of a decision tree depends, on the quality of the initial data which are incomplete, incorrect or non relevant data inevitably leads to erroneous results. The proposed model develops an ant colony algorithm integrated with GA for the discovery of spatial trend patterns found in a network traffic risk analysis database. The proposed ant colony based spatial data mining algorithm applies the emergent intelligent behavior of ant colonies. The experimental results on a network traffic (trend layer) spatial database show that our method has higher efficiency in performance of the discovery process compared to other existing approaches using non-intelligent decision tree heuristics.
Keywords: Spatial data mining, Network Traffic, ACO, GA
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