K-MEANS Fermé

Question

Bonjour,
 j' ai une centaine de documents  que je stocke sur une base de données Access et je veux les classifier avec l'algorithme K-MEANS en se basant sur la similarité . je rencontre un  problème a comment implémenter cet algorithme en java.   
Si vous avez une idée je suis preneur.
Merci d'avance.

mohamed · Answer

bonjour, il y'auna beaucoup de codes sources que vous pouvez les consulté. Moi aussi j'ai besoin de de kMEANS. package kc.test.kmeans. Si vous avez un autre code vous pouvez le m'envoyer par mail. Si vous pouvez aussi on peux se contacter pour s'aider. Merci import java.util.ArrayList; import java.util.Random; import java.awt.geom.Point2D; import voronoi.Vertex; import voronoi.Coord; import voronoi.Voronoi; /* The actual k-means algorithm implementation */ public class KMeansAlgorithm { /** * array containing all points */ private ArrayList m_points; /** * array containing the centroids */ private ArrayList m_clusters; /** * wether the algorithm is initialized or not */ private boolean m_initialized; /** * contains the voronoi diagram */ private Vertex[] m_diagram; /** * create a new algorithm instance */ public KMeansAlgorithm() { m_initialized = false; } /** * Initialize the algorithm with points and clusters * @param points * @param clusters */ void init(ArrayList points, ArrayList clusters) { m_points = points; m_clusters = clusters; m_initialized = true; } /** * Perform one step of the algorithm * @return the number of changes in cluster association occured in this step */ public int step() { // clear point list in centers for (Centroid c : m_clusters) { c.clearPoints(); } int changes = 0; // calculate for each point the smallest distance to a cluster centroid for (Point p : m_points) { double distance = Double.MAX_VALUE; Centroid min_center = null; for (Centroid c : m_clusters) { double d = p.getP().distance(c.getP()); if (d < distance) { distance = d; min_center = c; } } // this is a hack at the moment, changes in cluster association is at the moment // tracked by comparing the color information if (!p.getColor().equals(min_center.getColor())) changes++; p.setColor(min_center.getColor()); min_center.addPoint(p); } // calculate mean center for each cluster for (Centroid c : m_clusters) { c.addTrack((Point2D) c.getP().clone()); int num_points = c.getPoints().size(); if (num_points > 0) { double mean_x = 0.0; double mean_y = 0.0; for (Point p : c.getPoints()) { mean_x += p.getP().getX(); mean_y += p.getP().getY(); } mean_x = mean_x / num_points; mean_y = mean_y / num_points; c.getP().setLocation(mean_x, mean_y); } else { // if a centroid has no points attached, place it near another centroid with // many points Centroid max = c; int cnt = 0; for (Centroid other_center : m_clusters) { int num = other_center.getPoints().size(); if (num > cnt) { cnt = num; max = other_center; } } Random r = new Random(); int devX = r.nextInt(20) - 10; int devY = r.nextInt(20) - 10; c.getP().setLocation(max.getP().getX() + devX, max.getP().getY() + devY); } } calcVoronoiDiagram(); return changes; } /** * Perform a complete run of the algorithm. * For demo purposes a maximum iteration count of 20 is assumed. * @return the number of steps until the algorithm converged */ public int run() { int i, maxIterations, diff; i = 0; maxIterations = 20; do { i++; diff = step(); System.out.println("Iteration #" + i + ", changes = " + diff); } while (diff > 0 && i < maxIterations); return i; } /** * Get the init state of the algorithm * @return the init state */ public boolean isInitialized() { return m_initialized; } /** * Set the init state * @param val the new state */ public void setInit(boolean val) { m_initialized = val; m_diagram = null; } /** * Calculate the voronoi diagram for the current state of the algorithm */ private void calcVoronoiDiagram() { Coord[] t = new Coord[m_clusters.size()]; int i = 0; for (Centroid c : m_clusters) { Coord coord = new Coord(); coord.x = c.getP().getX(); coord.y = c.getP().getY(); t[i++] = coord; } m_diagram = Voronoi.generate(t); } public Vertex[] getDiagram() { return m_diagram; } }

K-MEANS

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