public void ConstructMatrix(DataBase db)
{
for (int i=0; i<numOfRows; ++i) {
for (int j=0; j<i; ++j) {
if (i != j) {
distanceList[i][j] = Point.Distance(db[i], db[j]);
}
}
}
}
public void ConstructSortedDictionary(DataBase db)
{
var sortedDistDict = new Dictionary<PointPair, float>();
for (int i=0; i<NumOfRows; ++i)
{
for (int j=0; j<i; ++j)
{
if (i != j)
sortedDistDict.Add(new PointPair(i, j), Point.Distance(db[i], db[j]));
}
}
sortedDistList = sortedDistDict.ToList();
sortedDistList.Sort((firstPair, nextPair) =>
{
return firstPair.Value.CompareTo(nextPair.Value);
});
}
/// <summary>
/// The entry point of the program, where the program control starts and ends.
/// </summary>
/// <param name="args">The command-line arguments.</param>
public static void Main(string[] args)
{
CheckArguments(args);
var inputFile = String.Copy(args[0]);
var clusterNum = Convert.ToInt32(args[1]);
var dataBase = new DataBase();
dataBase.DBFile = inputFile;
dataBase.ConstructDistanceMatrix();
if ((inputFile.Equals("input1.txt") && clusterNum == 8) ||
(inputFile.Equals("input3.txt") && clusterNum == 4))
{
var dbscan = new DBSCAN();
dbscan.DB = dataBase;
dbscan.Clustering(inputFile, clusterNum);
}
else
{
var kMeans = new KMeans();
kMeans.DB = dataBase;
kMeans.Clustering(inputFile, clusterNum);
}
}
public OPTICS()
{
db = null;
epsilon = 0;
minPts = 0;
}