Agents and Data Mining Interaction 

Abstract: The system that monitors the events occurring in a computer system or a network and analyzes the events for sign of intrusions is known as Intrusion Detection System (IDS). The IDS need to be accurate, adaptive, and extensible. Although many established techniques and commercial products exist, their effectiveness leaves room for improvement. A great deal of research has been carried out on intrusion detection in a distributed environment to palliate the drawbacks of centralized approaches. However, distributed IDS suffer from a number of drawbacks e.g. , high rates of false positives, low efficiency, etc. In this paper, we propose a distributed IDS that integrates the desirable features provided by the multi-agent methodology with the high accuracy of data mining techniques. The proposed system relies on a set of intelligent agents that collect and analyze the network connections, and data mining techniques are shown to be useful to detect the intrusions. Carried out experiments showed superior performance of our distributed IDS compared to the centralized one.

Abstract: Monitoring the electrodermal activity is increasingly accomplished in agent-based experimental settings as the skin is believed to be the only organ to react only to the sympathetic nervous system. This physiological signal has the potential to reveal paths that lead to excitement, attention, arousal and anxiety. However, electrodermal analysis has been driven by simple feature-extraction, instead of using expressive models that consider a more flexible behavior of the signal for improved emotion recognition. This paper proposes a novel approach centered on sequential patterns to classify the signal into a set of key emotional states. The approach combines SAX for pre-processing the signal and hidden Markov models. This approach was tested over a collected sample of signals using Affectiva-QSensor. An extensive human-to-human and human-to-robot experimental setting is under development for further validation and characterization of emotion-centered patterns.

Abstract: Clustering is useful in tag based recommenders to reduce sparsity of data and by doing so to improve also accuracy of recommendation. Strategy for the selection of tags for clusters has an impact on the accuracy. In this paper we propose a KNN based approach for ranking tag neighbors for tag selection. We study the approach in comparison to several baselines by using two datasets in different domains. We show, that in both cases the approach outperforms the compared approaches.

Abstract: Auctions can be characterized by distinct nature of their feature space. This feature space may include opening price, closing price, average bid rate, bid history, seller and buyer reputation, number of bids and many more. In this paper, a price forecasting agent (PFA) is proposed using data mining techniques to forecast the end-price of an online auction for autonomous agent based system. In the proposed model, the input auction space is partitioned into groups of similar auctions by k-means clustering algorithm. The recurrent problem of finding the value of k in k-means algorithm is solved by employing elbow method using one way analysis of variance (ANOVA). Based on the transformed data after clustering, bid selector nominates the cluster for the current auction whose price is to be forecasted. Regression trees are employed to predict the end-price and designing the optimal bidding strategies for the current auction. Our results show the improvements in the end price prediction using clustering and regression tree approach.