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Ability to monitor and detect abnormalities accurately is important in a manufacturing process. This can be achieved by recognizing abnormalities in its control charts. This work is concerned with classification of control chart patterns (CCPs) by utilizing a technique known as Symbolic Aggregate Approximation (SAX) and an evolutionary based data mining program known as Self-adjusting Association...
Real-time burst detection over multiple window size is useful for analyzing data streams. Various burst detection methods have been proposed. However, they are not effective for real-time detection. This work proposes a new burst detection method that reduces computation by avoiding redundant data updates. It analyses an event on its occurrence, and detects the period where arrival frequency rises...
Mobile data relay agents (or ferries) have the potential to build wireless backbones over which sparsely located, unconnected nodes can communicate in a delay-tolerant fashion. Paths taken by agents determine the quality of such delay tolerant networks (DTNs). We consider the problem of determining agent paths that minimize the maximum pair wise latency in the network. We introduce the concept of...
Detecting communities from networks has been given great attention these years. The traditional approaches were always focusing on the node community, while some recent studies have shown great advantage of link community approach which partitions links instead of nodes into communities. We proposed a novel algorithm LBLC (local based link community) to detect link communities in the network based...
In a knowledge discovery process, interpretation and evaluation of the mined results are indispensable in practice. In the case of data clustering, however, it is often difficult to see in what aspect each cluster has been formed. This paper proposes a method for automatic and objective characterization or "verbalization" of the clusters obtained by mixture models, in which we collect conjunctions...
RELIEF is a very effective and extremely popular feature selection algorithm developed for the first time in 1992 by Kira and Rendell. Since then it has been modified and expanded in various ways to make it more efficient. But the original RELIEF and all of its expansions are for feature selection over labeled data for classification purposes. To the best of our knowledge, for the first time ever...
Feature selection has been applied in many domains, such as text mining and software engineering. Ideally a feature selection technique should produce consistent outputs regardless of minor variations in the input data. Researchers have recently begun to examine the stability (robustness) of feature selection techniques. The stability of a feature selection method is defined as the degree of agreement...
In this paper we reconsider the computations accomplished in a semantic schema. We reconsider also the computations in a master-slave systems of semantic schemas introduced in [6] as a cooperating system of such structures. We show that a master-slave system is adequate to represent distributed knowledge. To relieve this fact we describe such a system named DiSys implemented in Java by client-server...
Computing all diagnoses of an inconsistent ontology is important in ontology-based applications. However, the number of diagnoses can be very large. It is impractical to enumerate all diagnoses before identifying the target one to render the ontology consistent. Hence, we propose to represent all diagnoses by multiple sets of partial diagnoses, where the total number of partial diagnoses can be small...
SVMs with the general purpose RBF kernel are widely considered as state-of-the-art supervised learning algorithms due to their effectiveness and versatility. However, in practice, SVMs often require more training data than readily available. Prior-knowledge may be available to compensate this shortcoming provided such knowledge can be effectively passed on to SVMs. In this paper, we propose a method...
This paper presents a reactive multi agent approach to the platoon control problem for the linear configuration. Platoon is a train of vehicles composed of a head vehicle and a variable number of followers. Vehicle-to vehicle coupling is virtual. Each follower vehicle controls its movement by interacting only with the preceding one. To this end, platoon control was designed as a reactive multi agent...
We develop a set of solution techniques for real-time evacuation guidance of pedestrians during emergency, focusing on evacuation from buildings during a fire. We model the problem as an extension of a dynamic network flow by allowing for nodes and edges to expire over time. This captures evacuation situations where the spreading hazard renders parts of the network unavailable. We formally state the...
In this paper, we recall the dynamic island model concept, in order to dynamically select local search operators within a multi-operator genetic algorithm. We use a fully-connected island model, where each island is assigned to a local search operator. Selection of operators is simulated by migration steps, whose policies depend on a learning process. The efficiency of this approach is assessed in...
In this paper, we propose a Fast Parallel Branch and Bound algorithm (FPBB) for computing tree width. FPBB searches the elimination ordering space using depth-first search approach, and employs multithreading techniques to search smaller divided spaces simultaneously. In order to protect the shared hash table without increasing the running time, we have designed a special algorithm for the readers-writers...
The study of community detection has received more and more attention in recent years, the problem is very difficult and of great importance in many fields such as sociology, biology and computer science. But most of the algorithms proposed so far could not utilize the weight information within weighted networks, and many of them are so time-consuming that they are not fit for the large-scale networks...
We examine the problem of filtering for dynamic probabilistic systems using Markov Logic Networks. We propose a method to approximately compute the marginal probabilities for the current state variables that is suitable for online inference. Contrary to existing algorithms, our approach does not work on the level of belief propagation, but can be used with every algorithm suitable for inference in...
Mining distributed data streams is a focus of much research in recent years, and it has brought many challenging problems. One of these problems is just learning and maintaining the global patterns from multiple data streams in distributed environments. In this paper, we discuss micro-cluster based classifying problems in distributed data streams, and propose the methods to mine data streams in the...
In a world where an increasing number of transactions are made on the web, there is a need for a trust evaluation tool dealing with uncertainty, e.g., for customers interested in evaluating the trustworthiness of an unknown service provider throughout queries to other customers of unknown reliability. In this paper, we propose to estimate the trust of an unknown agent, say ??^D, through the information...
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