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Copy file name to clipboardExpand all lines: ClusterKit/Core/Algorithm/CKGridBasedAlgorithm.h
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/**
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A simple grid-based clustering algorithm with O(n) performance.
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The great advantage of grid-based clustering is its significant reduction of the computational complexity, especially for clustering very large data sets. The grid-based clustering approach differs from the conventional clustering algorithms in that it is concerned not with the data points but with the value space that surrounds the data points.
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The great advantage of grid-based clustering is its significant reduction of the computational complexity,
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especially for clustering very large data sets. The grid-based clustering approach differs from the conventional
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clustering algorithms in that it is concerned not with the data points but with the value space that surrounds
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the data points.
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This grid-based implementation consists of the following the steps:
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1. Iterate througth the annotations found in the given rect.
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2. Associate each annotation to a grid cell. The rect is partitioned in a finite number of cells using the cell size property at the given zoom level.
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2. Associate each annotation to a grid cell. The rect is partitioned in a finite number of cells using the cell
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size property at the given zoom level.
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3. Annotation are added to a centroid cluster {@see CKCentroidCluster} by default.
Copy file name to clipboardExpand all lines: ClusterKit/Core/Algorithm/CKNonHierarchicalDistanceBasedAlgorithm.h
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/**
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A simple clustering algorithm with O(nlog n) performance.
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Non-hierarchical distance analysis aims to find a grouping of annotations which minimises the distance between an annotation and its cluster. These algorithm will iteratively assign annotations to different groups while searching for the optimal distance.
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Non-hierarchical distance analysis aims to find a grouping of annotations which minimises the distance
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between an annotation and its cluster. These algorithm will iteratively assign annotations to different
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groups while searching for the optimal distance.
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1. Iterate througth the annotations that are not yet clusterized found in the given rect.
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2. Create a cluster with the center of the annotation.
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3. Add all items that are within a certain distance to the cluster.
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4. Move any items out of an existing cluster if they are closer to another cluster.
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CKNonHierarchicalDistanceBasedAlgorithm is an objective-c implementation of the non-hierarchical distance based clustering algorithm used by Google maps.
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CKNonHierarchicalDistanceBasedAlgorithm is an objective-c implementation of the non-hierarchical distance
The delegate of a CKClusterManager object may adopt the CKClusterManagerDelegate protocol. Optional methods of the protocol allow the delegate to manage clustering and animations.
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The delegate of a CKClusterManager object may adopt the CKClusterManagerDelegate protocol.
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Optional methods of the protocol allow the delegate to manage clustering and animations.
@param clusterManager The cluster manager object requesting the animation.
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@param animations A block object containing the animation. This block takes no parameters and has no return value. This parameter must not be NULL.
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@param completion A block object to be executed when the animation sequence ends. This block has no return value and takes a single Boolean argument that indicates whether or not the animations actually finished before the completion handler was called. If the duration of the animation is 0, this block is performed at the beginning of the next run loop cycle. This parameter may be NULL.
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@param completion A block object to be executed when the animation sequence ends. This block has no return value and takes a single Boolean argument
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that indicates whether or not the animations actually finished before the completion handler was called. If the duration of the
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animation is 0, this block is performed at the beginning of the next run loop cycle. This parameter may be NULL.
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