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K-Means - Columbia Statistics
K-Means - Columbia Statistics

... • Dissimilarity/Similarity metric: Similarity is expressed in terms of a distance function, which is typically metric: d(i, j) • There is a separate “quality” function that measures the “goodness” of a cluster. • The definitions of distance functions are usually very different for interval-scaled, b ...
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... The main idea behind this algorithm is based on an observation that in most data sets there is a certain number of values having large number of occurrences within the data sets and a very large number of attributes with a very low number of occurrences. Therefore, the most representative values may ...
Moving Objects Databases
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... experiments conducted. One was update queries randomly generated for set of 10,000 cars for timestamps 1 to 4 at rates 1% and 5%. Other experiment was on different data sizes, 5k, 10k, 20k and 30k cars. Updates were taken at 1% and 5% rates and the algorithm proved to give most stable results for al ...
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... Rapidly sift through the data and discard unwanted data for later use and analysis (non-real-time data mining) Data mining techniques need to meet timing constraints Quality of service (QoS) tradeoffs among timeliness, precision and accuracy Presentation of results, visualization, real-time alerts a ...
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... • Data Mining is a powerful technology still undiscovered by many IT and database professionals • Turns data into intelligence • SQL Server 2005 and 2008 Analysis Services have been created with you in mind • Let’s mine for valuable gems of knowledge in our databases! ...
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Top 10 Data Mining Mistakes by John Elder

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Data Mining: introduction - UIC
Data Mining: introduction - UIC

... week after the item in question has been returned. Cheating: Cheating will not be tolerated. All work you submitted must be entirely your own. Any suspicious similarities between students' work will be recorded and brought to the attention of the Dean. The MINIMUM penalty for any student found cheat ...
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... We define a state as a consecutively repeating pattern of symbols that has several (significant) occurrences along the time series. In previous works, the term pattern can be found termed as primitive shape [8], frequent temporal pattern [9] and motif [10], and what we call states is more similar to ...
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Nonlinear dimensionality reduction



High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lie on an embedded non-linear manifold within the higher-dimensional space. If the manifold is of low enough dimension, the data can be visualised in the low-dimensional space.Below is a summary of some of the important algorithms from the history of manifold learning and nonlinear dimensionality reduction (NLDR). Many of these non-linear dimensionality reduction methods are related to the linear methods listed below. Non-linear methods can be broadly classified into two groups: those that provide a mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa), and those that just give a visualisation. In the context of machine learning, mapping methods may be viewed as a preliminary feature extraction step, after which pattern recognition algorithms are applied. Typically those that just give a visualisation are based on proximity data – that is, distance measurements.
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