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Time series classification has attracted much attention due to the ubiquity of time series. With the advance of technologies, the volume of available time series data becomes huge and the content is changing rapidly. This requires time series data mining methods to have low computational complexities. In this paper, we propose a parameter-free time series classification method that has a linear time...
To realize Electrocardiography (ECG) signals monitoring systems, compressive sensing (CS) is a new technique to reduce power of biosensors and data transmission. Instead of spending high complexity on reconstructing back to data domain to do signal analysis, compressed analysis (CA) exploits the data structure preserved by CS to directly analyze in the compressed domain. However, compressively-sensed...
Image Multi-label Classification (IMC) assigns a label or a set of labels to an image. The big demand for image annotation and archiving in the web attracts the researchers to develop many algorithms for this application domain. The Multi-Instance Multi-Label Learning (MIML) is an important type of machine learning framework proposed recently for IMC. In this framework, an image is described with...
Public policy is the critical key of the welfare programs. It is also a powerful instrument to achieve a feasible national competitiveness. Unfortunately, many public policy making processes does not utilize an appropriate data and tool in holistic and systematical approach. This research will focus on creating a comprehensive conceptual framework for public policymaking based on data and system approach...
One of the most classic algorithms for association rules mining is the Apriori algorithm. But it can't satisfy the requirement as the increasing scale of the data. It has some disadvantages such as scanning database too many times, setting support and confidence thresholds artificially. Particle swarm optimization is one of the classic heuristic algorithms and some researchers has used it to association...
This paper addresses the problem of semi-automatic image registration on planetary images. A joint feature-based and area-based approach is proposed. Firstly, the most relevant craters are extracted from the two images to register, and then, registration is performed in two steps. The first step matches the craters extracted from the images based on a generalized Hausdorff distance. In the second...
Organization with considerable investment into data warehousing, the influx of various data types and forms requires certain ways of prepping data and staging platform that support fast, efficient and volatile data to reach its targeted audiences or users of different business needs. Extract, Transform and Load (ETL) system proved to be a choice standard for managing and sustaining the movement and...
In an IoT environment, process analysis becomes more difficult as a process usually spans over a set of autonomous and distributed sensors. This paper consummates our previous service hyperlink model, to encapsulate dependencies among events generated from services. To effectively discover service hyperlinks, we transform the service hyperlink discovery problem into a frequent sequence mining problem...
In this paper, an image spam detection method was proposed. The proposed method has several parts: key block extraction, feature extraction, multi-level spam classifier. Key block extraction is used to extract the important information from image spams. Since color is one of important visual information to identify produces for humans, it is measured as a feature. To deal with geometric transform,...
One of the main challenges in pattern recognition is handling variations in pose, which has been addressed in the past using exhaustive training, increasingly complex neural network architectures, or state space transformations, but often with limits on pose variation. The solution presented here implements complete pose invariance by estimating affine transform parameters and then registering samples...
This paper presents a method to improve Thai-English word alignment in statistical machine translation (SMT) for interrogative sentences in a parallel corpus. We utilize the Thai and English grammatical knowledge i.e. tense, part of speech (POS), and question inversion pattern. The proposed method handles the difference of Thai and English interrogative sentences using sentence transformation, interrogative...
Change distilling algorithms compute a sequence of fine-grained changes that, when executed in order, transform a given source AST into a given target AST. The resulting change sequences are used in the field of mining software repositories to study source code evolution. Unfortunately, detecting and specifying source code evolutions in such a change sequence is cumbersome. We therefore introduce...
With the launch and rapid development of a novel satellite WorldView-2, study on fusion of panchromatic and multispectral images from the satellite has become a hot spot. As there are generally a pair of contradictions existing in the panchromatic and multispectral image fusion method: either failure to avoid spectral distortion, or need of introducing a complex and time-consuming frequency decomposition...
In conventional fusion methods based on NonSubsampled Contourlet Transform (NSCT), low-frequency subband coefficient of an image fails to express sparsely the image's low-frequency information, not in favor of extracting source image features. To address this issue, an infrared and visible image fusion method based on NSCT and joint sparse representation (JSR) was proposed, in which, JSR transform...
In this paper, we address the preparation of ecological datasets for data mining. We propose a new adaptive method for automatic dataset construction using Erblet transform, which can be seen as a non-uniform filter bank where the center frequency and the bandwidth of each filter match the ERB (Equivalent Rectangular Bandwidth) scale, followed by data quality assessment using a tonality index (TI)...
Time series classification is an important task in data mining that has been traditionally addressed with the use of similarity-based classifiers. The 1-NN DTW is typically considered the most accurate model for temporal data. Nevertheless, some authors have recently proposed ingenious alternatives to the 1-NN DTW by using diversity of time series representation or by using DTW for feature extraction...
Many kinds of real world data can be modeled by a heterogeneous information network (HIN) which consists of multiple types of objects. Clustering plays an important role in mining knowledge from HIN. Several HIN clustering algorithms have been proposed in recent years. However, these algorithms suffer from one or moreof the following problems: (1) inability to model general HINs, (2) inability to...
Thanks to the rise of wearable and connected devices, sensor-generated time series comprise a large and growing fraction of the world's data. Unfortunately, extracting value from this data can be challenging, since sensors report low-level signals (e.g., acceleration), not the high-level events that are typically of interest (e.g., gestures). We introduce a technique to bridge this gap by automatically...
In this paper, a novel adequate and concise information extraction approach is explored to provide a promising alternative for manifesting the intrinsic structure of the cyclostationary signals, such as communication signals. A novel graph-based signal representation is proposed to interpret the spectral correlation function into a graph and its adjacency matrix. This graph can represent the proposed...
It is crucial for Internet company to provide highly reliable web-based services. The web-based services always have many components running in the large-scale infrastructure with complex interactions. As an indispensable part of high reliability, the diagnosis remains to be a thorny problem. With the growth of system scale and complexity, it becomes even more difficult. In this paper, we propose...
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