A clustering-based visualization of spatial patterns
... distributed all over the area. Moreover, in practice, instances of a colocation are rarely grouped in a single location. Instead, they may be several locations where the colocation frequently appears. In such cases, this method will construct an ”average” spatial representation which is not necessar ...
... distributed all over the area. Moreover, in practice, instances of a colocation are rarely grouped in a single location. Instead, they may be several locations where the colocation frequently appears. In such cases, this method will construct an ”average” spatial representation which is not necessar ...
Association Rule Mining using Improved Apriori Algorithm
... Hash function in the database. The user has to specify the minimum support to prune the database Itemset and deletes the unwanted Itemset. Then pruned database itemsets are grouped according to the transaction length. Apriori Mend algorithm is found to be more admirable than the traditional method A ...
... Hash function in the database. The user has to specify the minimum support to prune the database Itemset and deletes the unwanted Itemset. Then pruned database itemsets are grouped according to the transaction length. Apriori Mend algorithm is found to be more admirable than the traditional method A ...
Discovery of Climate Indices using Clustering
... pattern. See [16] for a more technical description. Also, for each pair of patterns, there is an associated value (called a singular value), which is greater than or equal to 0. The strongest patterns (or the patterns that capture the largest amount of variation in the data) are associated with the ...
... pattern. See [16] for a more technical description. Also, for each pair of patterns, there is an associated value (called a singular value), which is greater than or equal to 0. The strongest patterns (or the patterns that capture the largest amount of variation in the data) are associated with the ...
thesis - Cartography Master
... metropolis Shanghai, travellers take around 60 000 – 70 000 taxi trips daily. This large number of travelling events leaves a footprint by the means of data that can be used to discover the dynamic of a city. When the historic FCD data is supplemented with data about venues, then an insight into tra ...
... metropolis Shanghai, travellers take around 60 000 – 70 000 taxi trips daily. This large number of travelling events leaves a footprint by the means of data that can be used to discover the dynamic of a city. When the historic FCD data is supplemented with data about venues, then an insight into tra ...
An Explorative Parameter Sweep: Spatial-temporal Data
... simulations. This will be a challenge because of the high dimensionality associated with the simulations output. For the purpose of extracting features, it is not a straightforward task to analyze time series with several attributes (species) in a three dimensional space. In other words, our time se ...
... simulations. This will be a challenge because of the high dimensionality associated with the simulations output. For the purpose of extracting features, it is not a straightforward task to analyze time series with several attributes (species) in a three dimensional space. In other words, our time se ...
Nearest-neighbor chain algorithm
In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.