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The primary failure mechanism in brittle materials such as ceramics, granite and some metal alloys is through the presence of defects which result in crack formation and propagation under the application of load. We are interested in studying this process of crack propagation, interaction and coalescence, which degrades the strength of the specimen. Traditionally, engineering applications that study...
CANDECOMP/PARAFAC Decomposition (CPD) is one of the most popular tensor decomposition methods that has been extensively studied and widely applied. In recent years, sparse tensors that contain a huge portion of zeros but a limited number of non-zeros have attracted increasing interest. Existing techniques are not directly applicable to sparse tensors, since they mainly target dense ones and usually...
In machine learning, data augmentation is the process of creating synthetic examples in order to augment a dataset used to learn a model. One motivation for data augmentation is to reduce the variance of a classifier, thereby reducing error. In this paper, we propose new data augmentation techniques specifically designed for time series classification, where the space in which they are embedded is...
Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on...
The full behavior of software-intensive systems of systems (SoS) emerges during operation only. Runtime monitoring approaches have thus been proposed to detect deviations from the expected behavior. They commonly rely on temporal logic or domain-specific languages to formally define requirements, which are then checked by analyzing the stream of monitored events and event data. Some approaches also...
ID3 is a classical algorithm of decision tree of classification with fast speed and easily understandable classification results. ID3 based on information gain tend to select test attribute with a variety of values, thus unable to deal with continuous attributes. In order to solve the above problems, this paper introduces the support in rough sets to discretize the continuous attributes dynamically,...
Different from full periodic patterns, partial periodic patterns could ignore the occurrence of some events in time positions. In this paper, we have presented a gradually pruning algorithm (GPA) for reducing the number of candidate patterns in the mining process. It is based on the two-phased periodic utility upper-bound (PUUB) model and could avoid information loss. Compared to the original approach...
Negative sequential patterns (NSP) become increasingly important and most of the existing methods introduce so strict constraints that many meaningful patterns would be lost. In this paper, we loosen these constraints and solve a series of consequent problems. Firstly, negative containment is defined to determine whether a data sequence contains a negative sequence. Secondly, an efficient method to...
With the rapid development of intelligent transportation systems, modern society is at an unimaginable speed to produce massive data. How to make full use of valuable information in big data is particularly important. This paper summarizes the basic concepts of association rule mining and how to use the Apriori algorithm for correlation analysis in massive data. In addition, the Apriori algorithm...
In the last few years, with the emergence of ambient assisted living, the study of human behavioral pattern took a wide interest from research communities around the world. In many literatures, pattern recognition was widely adopted approach to implements in human behavior study from computing perspective. Pattern recognition brings a promising results in terms of accuracy for modeling human behavior...
Process Mining is a technique to automatically discover and analyze business processes from event logs. Discovering concurrent activities often uses process mining since there are many of them contained in business processes. Since researchers and practitioners are giving attention to the process discovery (one of process mining techniques), then the best result of the discovered process models is...
Clustering is an important task in data mining area, especially in the area of continuous stream of data, i.e. ?data stream?. However, some characteristic of this kind of data is neglected during the existing clustering approaches. The similarity in temporal dimension between entities is underestimated. Forgetting mechanism is adopted to remove the old patterns to save computation resources. However,...
Time series similarity measure is an essential issue in time series data mining, which can be widely used in various applications. With an eye to the fact that most current measures neglect the shape characteristic of time series, this paper proposes a shape based similarity measure. By introducing a shape coefficient into the traditional weighted dynamic time warping algorithm, an improved version,...
We present a new algorithm for discovering clusters in noisy data streams using dynamic and cluster-specific temporal decay factors. Our improvement helps identify and adapt to evolving trends by adapting the weighting of stream data based on both content attributes and temporal arrival patterns. Our experimental results show that the proposed algorithm can discover better quality clusters in noisy...
With the development of process recommendation, dynamic adaptation and automatic modeling, the requirement of explicit and formalized expression of activity dependence relation in the business domain is becoming more and more urgent. However, these relations more exist in the minds of domain experts or in the unstructured documents, which leads process modeling and adaptation are a time-consuming...
The pervasive imbalanced class distribution occurring in real-world stream applications, such as surveillance, security and finance, in which data arrive continuously has sparked extensive interest in the study of imbalanced stream classification. In such applications, the evolution of unstable class concepts is always accompanied and complicated by the skewed class distribution. However, most of...
Telecommunications fraud, a new type of crime, is showing a rising trend in recent years. However, research from data mining perspectives to detect such frauds is scarce, especially with the behavioral sequences considered. Though the call detail records (CDRs) in telecommunication is generally a snapshot, the history of a caller/callee can be treated as sequences. Indeed, the historical calling sequences...
This paper addresses the issue of diagnosing the transfer function of mass transit hub on the basis of Automated Fare Collection data. A spatial and temporal diagnosis approach is proposed. Our investigation is focused on ‘inter-’ and ‘intra-’ stations diagnoses about passenger flows and facilities. Station transfer function definition, inter-stations diagnosis algorithm and intra-station calculation...
Traditionally the data retrieval is achieved by searching the metadata with keywords, though it is often difficult for ordinary users to express professional and precise query demands in the water industry. Regarding this issue, this paper introduces an exploratory retrieval method called faceted search by gradually recommending relevant facets to the users. Firstly, a unified modeling algorithm is...
Advanced pattern mining to extract the hidden but useful information by using proper structure is vital important for efficient information mining in large-scale practical datasets. The existing algorithms have not been capable of effective solving the fuzziness uncertainty of items and confirming the appropriate structure of studied patterns. In order to generate more proper practical patterns, a...
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