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| en:iot-reloaded:k_means [2024/12/10 16:02] – blanka | en:iot-reloaded:k_means [2024/12/10 21:36] (current) – pczekalski |
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| ===== K-Means ===== | ===== K-Means ===== |
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| The first method discussed here is one of the most commonly used – K-means. K-means clustering is a method that splits the initial set of points (objects) into groups, using distance measure, which represents a distance from the given point of the group to the group's centre representing a group's prototype - centroid. The result of the clustering is N points grouped into K clusters, where each point has assigned a cluster index, which means that the distance from the point of the cluster centroid is closer than the distance to any other centroids of other clusters. Distance measure employs Euclidian distance, which requires scaled or normalised data to avoid the dominance of a single dimension over others. | The first method discussed here is one of the most commonly used – K-means. K-means clustering is a method that splits the initial set of points (objects) into groups, using distance measure, representing a distance from the given point of the group to the group's centre representing a group's prototype: centroid. The result of the clustering is N points grouped into K clusters, where each point has assigned a cluster index, which means that the distance from the point of the cluster centroid is closer than the distance to any other centroids of other clusters. Distance measure employs Euclidian distance, which requires scaled or normalised data to avoid the dominance of a single dimension over others. |
| The algorithm steps schematically are represented in the following figure {{ref>K-means_steps}}: | The algorithm steps schematically are represented in the following figure {{ref>K-means_steps}}: |
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| In the figure: | In the figure: |
| * **STEP 1:** Initial data set, where points do not belong to any of the clusters. | * **STEP 1:** Initial data set where points do not belong to any of the clusters. |
| * **STEP 2:** Cluster initial centres are selected randomly. | * **STEP 2:** Cluster initial centres are selected randomly. |
| * **STEP 3:** For each point, the closest cluster centre is selected, which is the point marker. | * **STEP 3:** For each point, the closest cluster centre, which is the point marker, is selected. |
| * **STEP 4:** Cluster mark is assigned to each point. | * **STEP 4:** Cluster mark is assigned to each point. |
| * **STEP 5:** The initial cluster centre is being refined to minimise the average distance to the cluster centre from each cluster point. As a result, cluster centres might not be physical points any more; instead, they become imaginary. | * **STEP 5:** The initial cluster centre is being refined to minimise the average distance to it from each cluster point. As a result, cluster centres might no longer be physical points; instead, they become imaginary. |
| * **STEP 6:** Cluster marks of the points are updated. | * **STEP 6:** Cluster marks of the points are updated. |
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