1. Python Programming in Context Chapter 7

2. Objectives To use Python lists as a means of storing data To implement a nontrivial data mining application To understand and implement cluster analysis To use visualization as a means of displaying patterns

3. Cluster Data points that have something in common Clusters are dissimilar to each other Use simple Euclidean distance to measure how close one point is to another Centroid is a point that represents a cluster (not necessarily a real data point)

4. Figure 7.1

5. Figure 7.2

6. Figure 7.3

7. Figure 7.4

8. Listing 7.1 def euclidD(point1, point2): sum = 0 for index in range(len(point1)): diff = (point1[index]-point2[index]) ** 2 sum = sum + diff euclidDistance = math.sqrt(sum) return euclidDistance

9. Figure 7.5

10. Listing 7.2 def readFile(filename): datafile = open(filename, "r") datadict = {} key = 0 for aline in datafile: key = key + 1 score = int(aline) datadict[key] = [score] return datadict

11. Indefinite Iteration Repeating a process an unknown number of times Control is based on a boolean expression Infinite loop is possible Any for loop can be written as a while loop

12. Listing 7.3 while : statement1 statement2... statementn

13. Figure 7.6

14. Listing 7.4 sum = 0 for anum in range(1,11): sum = sum + anum print(sum)

15. Listing7.5 sum = 0 anum = 1 #initialization while anum <= 10: #condition sum = sum + anum anum = anum + 1 #change of state print(sum)

16. Listing 7.6 sum = 0 anum = 1 while anum <= 10: sum = sum + anum print(sum)

17. Listing 7.7 def readFile(filename): datafile = open(filename, "r") datadict = {} key = 0 aline = datafile.readline() while aline != "": key = key + 1 score = int(aline) datadict[key] = [score] aline = datafile.readline() return datadict

18. Creating Clusters Decide on number of clusters Choose data points to be initial centroids Assign data points to be members of a centroid Recompute centroids Repeat

19. Listing 7.8 def createCentroids(k, datadict): centroids=[] centroidCount = 0 centroidKeys = [] while centroidCount < k: rkey = random.randint(1,len(datadict)) if rkey not in centroidKeys: centroids.append(datadict[rkey]) centroidKeys.append(rkey) centroidCount = centroidCount + 1 return centroids

20. Listing 7.9 def createClusters(k, centroids, datadict, repeats): for apass in range(repeats): print("****PASS",apass,"****") clusters = [] for i in range(k): clusters.append([]) for akey in datadict: distances = [] for clusterIndex in range(k): dist = euclidD(datadict[akey],centroids[clusterIndex]) distances.append(dist) mindist = min(distances) index = distances.index(mindist) clusters[index].append(akey) dimensions = len(datadict[1])

21. Listing 7.9 continued for clusterIndex in range(k): sums = [0]*dimensions for akey in clusters[clusterIndex]: datapoints = datadict[akey] for ind in range(len(datapoints)): sums[ind] = sums[ind] + datapoints[ind] for ind in range(len(sums)): clusterLen = len(clusters[clusterIndex]) if clusterLen != 0: sums[ind] = sums[ind]/clusterLen centroids[clusterIndex] = sums for c in clusters: print ("CLUSTER") for key in c: print(datadict[key], end=" ") print() return clusters

22. Figure 7.7

23. Listing 7.10 def clusterAnalysis(dataFile): examDict = readFile(dataFile) examCentroids = createCentroids(5, examDict) examClusters = createClusters(5, examCentroids, examDict, 3) clusterAnalysis("cs150exams.txt")

24. Visualizing Clusters Earthquake data Show clusters on a map Use turtle module to plot data

25. Figure 7.8

26. Listing 7.11 def visualizeQuakes(dataFile): datadict = readFile(dataFile) quakeCentroids = createCentroids(6, datadict) clusters = createClusters(6, quakeCentroids, datadict, 7) quakeT = turtle.Turtle() quakeWin = turtle.Screen() quakeWin.bgpic("worldmap.gif") quakeWin.screensize(448,266) quakeWin.setup(width=500, height=300) wFactor = (quakeWin.screensize()[0]/2)/180 hFactor = (quakeWin.screensize()[1]/2)/90 quakeT.hideturtle() quakeT.up() colorlist = ["red","green","blue","orange","cyan","yellow"] for clusterIndex in range(6): quakeT.color(colorlist[clusterIndex]) for akey in clusters[clusterIndex]: lon = datadict[akey][0] lat = datadict[akey][1] quakeT.goto(lon*wFactor,lat*hFactor) quakeT.dot() quakeWin.exitonclick()

27. Figure 7.9