data mining for teleconnections in global climate datasets
... 2.1 Data mining, spatial clustering, and association rules Data mining means to “mine” interesting patterns in large amount of data. In geographic science, these data contain temporal data (often time-series data) and spatial data (often in raster format). Spatial-temporal data mining is a research ...
... 2.1 Data mining, spatial clustering, and association rules Data mining means to “mine” interesting patterns in large amount of data. In geographic science, these data contain temporal data (often time-series data) and spatial data (often in raster format). Spatial-temporal data mining is a research ...
COMPARATIVE STUDY OF DATA MINING ALGORITHMS Gabriel
... candidate sets. However, in situations with prolific frequent patterns, long patterns, or quite low minimum support thresholds, an Apriori-like algorithm may still suffer from the following two nontrivial costs: It is costly to handle a huge number of candidate sets. For example, if there are 104 ...
... candidate sets. However, in situations with prolific frequent patterns, long patterns, or quite low minimum support thresholds, an Apriori-like algorithm may still suffer from the following two nontrivial costs: It is costly to handle a huge number of candidate sets. For example, if there are 104 ...
Predicting Globally and Locally: A Comparison of Methods for Vehicle Trajectory Prediction
... and other probabilistic methods that predict paths accurately. However, most methods rely on local structure of data, and use many extra features to improve prediction accuracy. In this paper we use only the basic spatio-temporal data stream. We advance the state-of-the-art by proposing the LapStrat ...
... and other probabilistic methods that predict paths accurately. However, most methods rely on local structure of data, and use many extra features to improve prediction accuracy. In this paper we use only the basic spatio-temporal data stream. We advance the state-of-the-art by proposing the LapStrat ...
Data Mining Techniques to Find Out Heart Diseases: An
... the Cleveland Clinic Foundation, Hungarian Institute of Cardiology, V.A. Medical Center and University Hospital of Switzerland. It provides 920 records in total. Originally, the database had 76 raw attributes. However, all of the published experiments only refer to 13 of these: Age, Sex, P, Trstbps, ...
... the Cleveland Clinic Foundation, Hungarian Institute of Cardiology, V.A. Medical Center and University Hospital of Switzerland. It provides 920 records in total. Originally, the database had 76 raw attributes. However, all of the published experiments only refer to 13 of these: Age, Sex, P, Trstbps, ...
Analysis of Student Result Using Clustering Techniques
... guideline for higher educational system to improve their decision-making processes. It can be used to analyze the existing work, identifying existing gaps and further works. The researchers may use the model to identify the existing area of research in the field of data mining in higher educational ...
... guideline for higher educational system to improve their decision-making processes. It can be used to analyze the existing work, identifying existing gaps and further works. The researchers may use the model to identify the existing area of research in the field of data mining in higher educational ...
a subspace clustering of high dimensional data
... overlapping problem but also limits the information loss to cope with the data coverage problem. The highdimensional data is inherently more complex in clustering, classification, and similarity search. It produces identical results irrespective of the order in which input records are presented and ...
... overlapping problem but also limits the information loss to cope with the data coverage problem. The highdimensional data is inherently more complex in clustering, classification, and similarity search. It produces identical results irrespective of the order in which input records are presented and ...
An Accelerated MapReduce-based K
... clustering methods. In this context, several parallel clustering methods have been designed in the literature [2, 4, 10, 15, 17, 18, 20, 24]. Most of these methods use the MapReduce [5], which is a programming model for processing large scale data by exploiting the parallelism among a cluster of mac ...
... clustering methods. In this context, several parallel clustering methods have been designed in the literature [2, 4, 10, 15, 17, 18, 20, 24]. Most of these methods use the MapReduce [5], which is a programming model for processing large scale data by exploiting the parallelism among a cluster of mac ...
a unified theory of data mining based on
... This is the standard way of representing graph data, namely by simply listing the set of edges that make up the graph. The same graph can be modeled (more efficiently?) as an Index on E(N,N): E(N,Nset) = {(n,Nsetn)|nN, Nsetn≡Set of nodes related to n} Then, if there are many edges, it may be more e ...
... This is the standard way of representing graph data, namely by simply listing the set of edges that make up the graph. The same graph can be modeled (more efficiently?) as an Index on E(N,N): E(N,Nset) = {(n,Nsetn)|nN, Nsetn≡Set of nodes related to n} Then, if there are many edges, it may be more e ...
from wheatonma.edu - CS Home
... distance matrix and Prim’s algorithm run in O(n2 ) complexity, and yet the ordering can be considered optimal. So in contrast to the 2D arrangement, which by Ankerst et al. [8] was shown to be NP-hard, this problem actually is easier in 3 dimensions due to the extra degree of freedom. This approach ...
... distance matrix and Prim’s algorithm run in O(n2 ) complexity, and yet the ordering can be considered optimal. So in contrast to the 2D arrangement, which by Ankerst et al. [8] was shown to be NP-hard, this problem actually is easier in 3 dimensions due to the extra degree of freedom. This approach ...
Automated interpretation of 3D laserscanned point clouds for plant organ segmentation
... A major part of the present work consists of providing metrics for comparison of histograms obtained from 3D laser point clouds, and using them for unsupervised learning for automated classification or clustering of plant organs. A common and widely used measure is the Euclidean distance, which is d ...
... A major part of the present work consists of providing metrics for comparison of histograms obtained from 3D laser point clouds, and using them for unsupervised learning for automated classification or clustering of plant organs. A common and widely used measure is the Euclidean distance, which is d ...
Android API Client for Fon11.com Literature Survey
... from existing databases. Stresses both query-optimization and data-management components as well as extensions such as language primitives. Data-Mining query invites the system to decide which portion of data to focus. Naïve implementations will result in execution of large decision-support qu ...
... from existing databases. Stresses both query-optimization and data-management components as well as extensions such as language primitives. Data-Mining query invites the system to decide which portion of data to focus. Naïve implementations will result in execution of large decision-support qu ...
Clustering - upatras eclass
... 185,72: distance from cluster 1 = sqrt( (182-185)^2 + (70.6-72)^2) = 3.31 (PUT in this cluster) 185,72: distance from cluster 2 = sqrt( (169-185)^2 + (58-72)^2) = 21.26 170, 56: distance from cluster 1 = sqrt( (182-170)^2 + (70.6-56)^2) = 18.89 170, 56: distance from cluster 2 = sqrt( (169-170)^2 + ...
... 185,72: distance from cluster 1 = sqrt( (182-185)^2 + (70.6-72)^2) = 3.31 (PUT in this cluster) 185,72: distance from cluster 2 = sqrt( (169-185)^2 + (58-72)^2) = 21.26 170, 56: distance from cluster 1 = sqrt( (182-170)^2 + (70.6-56)^2) = 18.89 170, 56: distance from cluster 2 = sqrt( (169-170)^2 + ...