ABSTRACT

Fast retrieval of the relevant information from the databases has always been a significant issue. Different techniques have been developed for this purpose, one of them is Data Clustering. In this paper Data Clustering is discussed along with its two traditional approaches and their analysis. Some applications of Data Clustering like Data Mining using Data Clustering and Similarity Searching in Medial Image Databases are also discussed along with a case study of Microsoft Windows NT Operating system. The implem ....etc

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please show me a power point presentation of the topic Data Clustering and its Applications ....etc

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The Application of Stream Data Time-Series Pattern Reliance Mining in Stock Market Analysis

With the rapid development of computer information technology, a new data model-the stream data emerged. The stream data is coming continuously, quickly, changing with time, and may be unpredictable and unlimited in the way. The emergence of stream data has brought new challenges from the nature ofthe data applied to static database technology and data mining technology. At present, the stream data has been widely used in telecommunications, financial ....etc

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Satellite Image Time Series Analysis Under Time Warping



Abstract

Satellite Image Time Series are becoming increasingly
available and will continue to do so in the coming years
thanks to the launch of space missions which aim at providing a
coverage of the Earth every few days with high spatial resolution.
In the case of optical imagery, it will be possible to produce land
use and cover change maps with detailed nomenclatures. However,
due to meteorological phenomena, such as clouds, these time serie ....etc

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Demand Forecasting: Time Series Models



PResented By:
Professor Stephen R. Lawrence
College of Business and Administration
University of Colorado



Forecasting Horizons
Long Term
5+ years into the future
R&D, plant location, product planning
Principally judgement-based



Medium Term
1 season to 2 years
Aggregate planning, capacity planning, sales forecasts
Mixture of quantitative methods and judgement


Short Term
1 day to 1 year, less than 1 season
Demand forecasting, staffing level ....etc

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Abstract—Existing distance measures of time series such as the euclidean distance, DTW, and EDR are inadequate in handling certain degrees of amplitude shifting and scaling variances of data items. We propose a novel distance measure of time series, Spatial Assembling Distance (SpADe), that is able to handle noisy, shifting, and scaling in both temporal and amplitude dimensions. We further apply the SpADe to the application of streaming pattern detection, which is very useful in trend-related analysis, sensor networks, and video surveillance. ....etc

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Abstract:

This paper presents a data clustering method named BIRCH(Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrate that these especially suitable for very large data bases . BIRCH icrementally and dynamically clusters incoming multy dimensional metric data points to trie to produce the best qulity clustering with available recourses (i.e., available memory and time constraints).

BIRCH can typically find a good clustering with a single-scan of the data and improve the quality further with few additiona ....etc

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A little bit of history….

Jean Baptiste Joseph Fourier (1768 – 1830)

MGP: Leibniz - Bernoulli - Bernoulli - Euler - Lagrange - Fourier – Dirichlet - ….

1787: Train for priest (Left but Never married!!!).
1793: Involved in the local Revolutionary Committee.
1974: Jailed for the first time.
1797: Succeeded Lagrange as chair of analysis and ....etc

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Abstract

Prediction of an event at a time series is quite important for engineering and economy problems. Time series data mining combines the fields of time series analysis and data mining techniques. This method creates a set of methods that reveal hidden temporal patterns that are characteristic and predictive of time series events. Time series data mining examines the time series in a phase space. In this paper, a prediction algorithm using time series data mining based on fuzzy logic is proposed. Earthquake prediction has been ....etc

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