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Process mining serves a bridge between data mining and business process modeling. The goal is to extract process related knowledge from event data stored in information systems. One of the most challenging process mining tasks is process discovery, i.e., the automatic construction of process models from raw event logs. Today there are dozens of process discovery techniques generating process models...
In this article we present a new approach to extract points that belongs to several ellipses or circles presented on a same image and with the presence of outliers. Each geometric form is extracted by means of a robust fitting, that is a nonlinear optimization problem, solved with two different heuristics: differential evolution and RANSAC. Once the geometric form is fitted, its points are extracted...
The automatic extraction of video structure from content is of key importance to enable a variety of multimedia services that span from search and retrieval to content manipulation. An unsupervised independent unimodal clustering method for anchorpersons detection and differentiation in newscasts is presented in this paper. The algorithm exploits audio, frame and face information to identify major...
The real-world activity data collection using simple and ubiquitous sensors is in general a passive process. During this process, a set of sensors are embedded with home appliances while the experience sampling tool (ESM) is provided to the user for acquiring self-reported activity label. No direct or active observation is provided to label the activity and to see the corresponding sensor activations...
In this paper we address the topic of feature extraction in 3D point cloud data for object recognition and pose identification. We present a novel interest keypoint extraction method that operates on range images generated from arbitrary 3D point clouds, which explicitly considers the borders of the objects identified by transitions from foreground to background. We furthermore present a feature descriptor...
Clustering has been used widely in pattern recognition, image processing, data mining and so on. Many clustering algorithms are sensitive to outlier faults in noisy environments. In this paper, we propose a new algorithm called sample weighted possibilistic fuzzy c-means clustering (SWPFCM). Based on combination sample weighting and a suitable for noise environment of initialization clustering center...
The research of a determining system of flow image was made, which aimed at tracer particle image obtained by experiment, using digital image processing and analysis, pattern recognition and artificial intelligence. Qualitative and quantitative analysis was used on flow field of torque converter with this system. According to the characteristic of experiment, identifying and tracking the trail of...
Process mining has been widely applied in lots of fields, which can discover workflow models from event logs, helps to design or re-design process models, and brings convenience to workflow management system. The main function of process mining algorithms is to provide us with valuable objective information hidden in event logs, which plays a crucial important role on the implementation of new operation...
The pictures extracted from the plant roots often have quite big noises and often affected greatly by the light intensity, neither could we achieve satisfactory results by using the traditional image edge detection method to detect the edge information. To this issue, a method for edge detection and its application in edge image detection for plant roots via gabor wavelet theory is proposed, which...
Empirical techniques for characterizing electrical energy use now play a key role in reducing electricity consumption, particularly miscellaneous electrical loads, in buildings. Identifying device operating modes (mode extraction) creates a better understanding of both device and system behaviors. Using clustering to extract operating modes from electrical load data can provide valuable insights into...
Process discovery is crucial for understanding how business operations are performed and how to improve them. The opportunity to discover process models exists given that many systems underlying the execution of process steps log their execution times. However, there are many challenges to discover the actual processes particularly complex ones and without making unrealistic assumptions. In this paper...
It has great significance to efficiently distinguish the type of the samples' data in the decision table after the discretization for the course of machine learning and data mining afterwards. This paper puts forward an annotation method of distinguishing the data type based on attributes importance and the samples entropy, and processed the simulation test using part of the UCI database which was...
For celestial spectra are vectors in a several-thousand-dimensional space with a mass of redundancy and usually contaminated with various noises, feature extraction is an essential procedure in automatic spectra processing. We investigated the feature extraction problem for Quasar and Galaxy spectra classification. The available methods in literature can be loosely classified into the following types:...
Discovering clusters of varyingly shapes, sizes and densities in a data set is still a challenging problem for density-based algorithms. Recently presented approaches either require the input parameters involving the information about the structure of the data set, or are restricted to two-dimensional data. In this paper, we present a density-based clustering algorithm, which uses the fuzzy proximity...
Specifications carrying formal parameters that are bound to concrete data at runtime can effectively and elegantly capture multi-object behaviors or protocols. Unfortunately, parametric specifications are not easy to formulate by nonexperts and, consequently, are rarely available. This paper presents a general approach for mining parametric specifications from program executions, based on a strict...
The support vector regression (SVR) framework is proposed as the basis for an echo correction technique. Its generalization capabilities in input-output knowledge discovery make the method suitable to solve the problem of the unwanted reflections in measurement systems and to recover the real values of the antenna under test. Experimental validation is also presented to show the efficiency of the...
This paper proposes a robust blind watermarking scheme for 2D-vector data used in Geographical Information Systems (GIS). The proposed method embeds two types of watermarks that complement each other into the vector data. It preserves the fidelity of the vector data using an intersection test. Simulation results show that our method is resistant to common attacks such as translations, scaling, rotation,...
Very Fast Decision Tree (VFDT) in data stream mining has been widely studied for more than a decade. VFDT in essence can mine over a portion of an unbounded data stream at a time, and the structure of the decision tree gets updated whenever new data feed in; hence it can predict better upon the input of fresh data. Inherent from traditional decision trees that use information gains for tree induction,...
Road-vehicle noise samples generated by common vehicles under various operating conditions were replayed using headphones. Ten subjects were instructed to describe and quantify their subjective responses to the noises. A wealth of numerical perceptual information was obtained and investigated by principal component analysis. Based on the human auditory-brain system theory, the physical factors extracted...
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