PPT Presentation
... 93-041 0337 - 93-041 0340 BL = 33 93-041 0341 - 93-041 0348 PS/BL = 13 PSBL 93-041 0349 - 93-041 0652 BL = 33 ...
... 93-041 0337 - 93-041 0340 BL = 33 93-041 0341 - 93-041 0348 PS/BL = 13 PSBL 93-041 0349 - 93-041 0652 BL = 33 ...
Eigen decomposition, k-means, object oriented implementation and
... data and knowledge engineering. Generally the real time data objects are high dimensional. Typical clustering techniques are available to process these high dimensional data, but implementation of these techniques don’t provide modularity, data security, code reusability and require much more comput ...
... data and knowledge engineering. Generally the real time data objects are high dimensional. Typical clustering techniques are available to process these high dimensional data, but implementation of these techniques don’t provide modularity, data security, code reusability and require much more comput ...
Scalable Sequential Spectral Clustering
... quadratic space and time because of the computation of pairwise distance of data points. This process is easy to sequentialize. Specifically, we can keep only one sample of data xi in the memory and then load all the other data from the disk sequentially and compute the distances from xi to all the o ...
... quadratic space and time because of the computation of pairwise distance of data points. This process is easy to sequentialize. Specifically, we can keep only one sample of data xi in the memory and then load all the other data from the disk sequentially and compute the distances from xi to all the o ...
14_clustering
... • Supervised learning used labeled data pairs (x, y) to learn a function f : X→Y. • But, what if we don’t have labels? • No labels = unsupervised learning • Only some points are labeled = semi-supervised learning –Getting labels may be expensive, so we only get a few • Clustering is the unsupervised ...
... • Supervised learning used labeled data pairs (x, y) to learn a function f : X→Y. • But, what if we don’t have labels? • No labels = unsupervised learning • Only some points are labeled = semi-supervised learning –Getting labels may be expensive, so we only get a few • Clustering is the unsupervised ...
Innovations in Data Collection and Management
... • Common vision - reduce time in data collection processing to provide more resource for analysis ...
... • Common vision - reduce time in data collection processing to provide more resource for analysis ...
Basic Concepts in Data Mining
... Similarity and Distance Measures •! Most clustering algorithms depend on a distance or similarity measure, to determine (a) the closeness or “alikeness” of cluster members, and (b) the distance or “unlikeness” of members from different clusters. •! General requirements for any similarity or distance ...
... Similarity and Distance Measures •! Most clustering algorithms depend on a distance or similarity measure, to determine (a) the closeness or “alikeness” of cluster members, and (b) the distance or “unlikeness” of members from different clusters. •! General requirements for any similarity or distance ...
PDF - Bentham Open
... with the same data set, along with the increase of the cluster nodes the processing time is reducing. When processing the data set whose size is 100M, the processing time of the cluster with only 1 node is nearly similar to that with 2 nodes or 3 nodes. However, the processing of 1000M data set is v ...
... with the same data set, along with the increase of the cluster nodes the processing time is reducing. When processing the data set whose size is 100M, the processing time of the cluster with only 1 node is nearly similar to that with 2 nodes or 3 nodes. However, the processing of 1000M data set is v ...
A Network Algorithm to Discover Sequential Patterns
... task for the user. The user must specify a minimum support threshold to find the desired patterns. A useless output can be expected by pruning either too many or too few items. The process must be repeated interactively, which becomes very time consuming for large databases. The association rules’ s ...
... task for the user. The user must specify a minimum support threshold to find the desired patterns. A useless output can be expected by pruning either too many or too few items. The process must be repeated interactively, which becomes very time consuming for large databases. The association rules’ s ...
dm19-data-mining-and
... Data Mining and Discrimination Can discrimination be based on features like sex, age, national origin? In some areas (e.g. mortgages, employment), some features cannot be used for decision making In other areas, these features are needed to assess the risk factors E.g. people of African des ...
... Data Mining and Discrimination Can discrimination be based on features like sex, age, national origin? In some areas (e.g. mortgages, employment), some features cannot be used for decision making In other areas, these features are needed to assess the risk factors E.g. people of African des ...
Temporal Data Mining for Small and Big Data Theophano Mitsa, Ph.D.
... EEG signals and detect patterns. The fractal dimension was chosen because of the chaotic nature of the signals. In [7], 3 methods to classify EEG time series were compared. 1. Linear Discriminant Analysis. 2. Neural Networks. 3. Support Vector Machines. SVMs gave the best results. ...
... EEG signals and detect patterns. The fractal dimension was chosen because of the chaotic nature of the signals. In [7], 3 methods to classify EEG time series were compared. 1. Linear Discriminant Analysis. 2. Neural Networks. 3. Support Vector Machines. SVMs gave the best results. ...
A Novel Path-Based Clustering Algorithm Using Multi
... Despite many clustering algorithms being proposed, K-means is still widely used and is one of the most popular clustering algorithms [2]. This is because it is an efficient, simple algorithm and provides successful results in many practical applications. However, K-means is only good at clustering c ...
... Despite many clustering algorithms being proposed, K-means is still widely used and is one of the most popular clustering algorithms [2]. This is because it is an efficient, simple algorithm and provides successful results in many practical applications. However, K-means is only good at clustering c ...
PPT
... high dimensionality data of few samples. Variable selection is the first issue that we have been trying to solve. Computational expensive looking for the best model (2^d), where d is de number of dimensions. Applying SQL optimizations and data layout modifications, we obtain less than 3 seconds sele ...
... high dimensionality data of few samples. Variable selection is the first issue that we have been trying to solve. Computational expensive looking for the best model (2^d), where d is de number of dimensions. Applying SQL optimizations and data layout modifications, we obtain less than 3 seconds sele ...
Data Mining: Concepts & Techniques
... • Nominal Variables – A nominal variable takes on more than two states. For example, the eye color of a person can be blue, brown, green or grey eyes – These states may be coded as 1, 2, ..., M, however their order and the interval between any two states do not have any meaning ...
... • Nominal Variables – A nominal variable takes on more than two states. For example, the eye color of a person can be blue, brown, green or grey eyes – These states may be coded as 1, 2, ..., M, however their order and the interval between any two states do not have any meaning ...
CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE
... Data Loading implies physical movement of the data from the computer(s) storing the source database(s) to that which will store the data warehouse database, assuming it is different. (1 mark) Data Loading Types: ...
... Data Loading implies physical movement of the data from the computer(s) storing the source database(s) to that which will store the data warehouse database, assuming it is different. (1 mark) Data Loading Types: ...
Predicting Globally and Locally: A Comparison of Methods for Vehicle Trajectory Prediction
... Given an in-progress taxi trajectory, the methods presented facilitate predictions about the future movement of the vehicle. To simulate this task, a collection of partial trajectories (e.g. Figure 4) is generated from complete trajectories in the test set. A set of relevant policy vectors is genera ...
... Given an in-progress taxi trajectory, the methods presented facilitate predictions about the future movement of the vehicle. To simulate this task, a collection of partial trajectories (e.g. Figure 4) is generated from complete trajectories in the test set. A set of relevant policy vectors is genera ...
slides - University of California, Riverside
... • Spend 2 weeks adjusting the parameters (I am not impressed) ...
... • Spend 2 weeks adjusting the parameters (I am not impressed) ...
The Earth-Observation Image Librarian (EOLib): The data mining
... Provides a projection of the entire database based on primitive feature vectors Representation of the data in the 3D space (dimensionality reduction) Interactive exploration and analysis of very large, high complexity data sets This allows the user: To browse the image archive To find sc ...
... Provides a projection of the entire database based on primitive feature vectors Representation of the data in the 3D space (dimensionality reduction) Interactive exploration and analysis of very large, high complexity data sets This allows the user: To browse the image archive To find sc ...
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.