File Name: time series and sequence data in data mining .zip
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In the last decade, there has been an explosion of interest in Mining time series data. While these many different techniques used to solve these problems use a multitude of different techniques, they all have one common factor; they require some high level representation of the data, rather than the original raw data. These high level representation are necessary as a feature extraction step, or simply to make the storage, transmission, and computation of massive dataset feasible. A multitute of representations have been proposed in the literature, including spectral transform, wavelets transforms, piecewise polynomials, eigenfunctions, and symbolic mappings.
Mining Stream, Time-series, and Sequence Data
In the last decade, there has been an explosion of interest in Mining time series data. While these many different techniques used to solve these problems use a multitude of different techniques, they all have one common factor; they require some high level representation of the data, rather than the original raw data.
These high level representation are necessary as a feature extraction step, or simply to make the storage, transmission, and computation of massive dataset feasible. A multitute of representations have been proposed in the literature, including spectral transform, wavelets transforms, piecewise polynomials, eigenfunctions, and symbolic mappings.
Aggarwal, C, Hinneburg, A. On the surprising behavior of distance metrics in high dimensional space. Google Scholar. Agrawal, R. Efficient Similarity Search in Sequence Data bases. Berndt, D. Bollobas, B. Nordic Jour.
Brin, S. Near neighbor search in large metric spaces. Proceedings of 21 st VLDB; Chakrabarti, K. Locally adaptive dimensionality reduction for indexing large time series databases. Volume 27, Issue 2, June Chan, K. Efficient time series matching by wavelets. Chang, C. Chiu, B. Probabilistic discovery of time series motifs. Ciaccia, P. M-tree: An efficient access method for similarity search in metric spaces. Proceedings of 23 rd VLDB; , pp.
Crochemore, M. Speeding up two string-matching algorithms. Algorithmica; ; Vol. Dasgupta, D. Debregeas, A. Interactive interpretation of kohonen maps applied to curves. Faloutsos, C, Jagadish, H. A signature technique for similarity-based queries. Faloutsos, C, Ranganaihan, M. Fast subsequence matching in time-series databases.
Ge, X. Geurts, P. Pattern extraction for time series classification. Goldin, D. Guralnik, V, Srivastava, J. Event detection from time series data. Huhtala, Y. Mining for similarities in aligned time scries using wavelet. Hochheiser, H.
Interactive Exploration of Time-Sereis Data. Indyk, P. Identifying representative trends in massive time series data sets using sketches. Jagadish, H. Similarity-Based Queries. Kahveci, T. Variable length queries for time series data. Kalpakis, K. Kanth, K. Dimensionality reduction for similarity searching in dynamic databases. Keogh, E. Exact indexing of dynamic time warping.
Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems ; 3: — Proceedings of ICDM; Towards Parameter-Free Data Mining. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. Korn, F. Efficiently supporting ad hoc queries in large datasets of time sequences. Kruskal, J. Addison-Wesley, Lin, J. Ma, J. Online Novelty Detection on Temporal Sequences.
Nievergelt, H, Hinterberger, H. The grid file: An adaptable, symmetricmultikey file structure. ACM Trans. Database Systems; ; 9 1 : 38— Palpanas, T. Pavlidis, T. Segmentation of plane curves. C 8 , pp. MathSciNet Google Scholar. Popivanov, I. Similarity search over time series data using wavelets. Rafiei, D. Efficient retrieval of similar time sequences using DFT.
Ratanamahatana, C. Ripley, B. Pattern recognition and neural networks. Robinson, J. The K-d-b-tree: A search structure for large multidimensional dynamic indexes. Shahabi, C.
Mining Time Series Data
Skip to content. All Homes Search Contact. Each of these properties adds a challenge to data stream mining. Unit Multi-step methodologies and techniques, and multi-scan algorithms, suitable for knowledge discovery and data mining, cannot be readily applied to data streams. They are. Data mining can also be applied to other forms of data e. The term is actually a misnomer.
Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Korn and Gks and Dobra and Garofalakis and R. Our previous chapters introduced the basic concepts and techniques of data mining. The techniques studied, however, were for simple and structured data sets, such as data in relational databases, transactional databases, and data warehouses.
mining data streams in dwdm
Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning , statistics , and database systems. The term "data mining" is a misnomer , because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction mining of data itself. The book Data mining: Practical machine learning tools and techniques with Java  which covers mostly machine learning material was originally to be named just Practical machine learning , and the term data mining was only added for marketing reasons.
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Sequential data from Web server logs, online transaction logs, and performance measurements is collected each day. This sequential data is a valuable source of information, as it allows individuals to search for a particular value or event and also facilitates analysis of the frequency of certain events or sets of related events. Finding patterns in sequences is of utmost importance in many areas of science, engineering, and business scenarios. Pattern Discovery Using Sequence Data Mining: Applications and Studies provides a comprehensive view of sequence mining techniques and presents current research and case studies in pattern discovery in sequential data by researchers and practitioners.
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