SPSS cluster analysis how to do
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Help SPSS cluster analysis
Center of gravity method, the method of the longest and shortest distance and whatnot, see everyone to write a brief tutorial on the site, but not detailed enough. It is difficult to understand, just because the writing of the fuzzy my operation is the same but the results are not the same as the answer. Very anxious. Looking forward to experts to tell me ！！！！！
1. The shortest distance method defines the distance between two classes as the one that minimizes the distance between all the cases in one class and all the cases in the other class. The disadvantage is that it has a tendency to link aggregation, because the distance between the class and the class is the shortest of all the distances, after the merger of the two classes, the distance between it and the other class shrinks, so it is easy to form a larger class. So this method is not very effective and not used in practice.
2. The longest distance method defines the distance between classes as the distance between the two farthest cases of two classes. The longest distance method overcomes the drawbacks of the shortest distance method of link aggregation, the distance between the two classes merged with the other class is the largest distance between the original two classes, increasing the distance between the merged class and the other class.
3. Average linkage method, the shortest and longest distance method are only two cases to determine the distance between the two classes, do not make full use of all the cases of information, the average linkage method of the distance between the two classes is defined as the average of the distance between all the cases of the two classes, no longer rely on the distance between the special points, there is a tendency to small variance of the class together, the effect is better, more widely used. .
4. Center of gravity method, the distance between the two classes is defined as the distance between the center of gravity of the two classes, the center of gravity of each class is the center of gravity of all the cases in the class in the mean value of each variable represents the point. Unlike the above three, the center of gravity is recalculated for each merger. The center of gravity method is also less affected by special points. The main disadvantage of the center of gravity method, which requires the use of Euclidean distances, is that there is no guarantee that the distances between the merged classes will increase monotonically during the clustering process, i.e., the distance between the two classes in the current merger may be smaller than the distance between the two classes in the previous merger.
5. The sum-of-squares method, also known as Wald’s method. The idea is that the sum of squared deviations should be smaller for cases within the same category and larger for cases between different categories. The solution process is to first make each case into a class of its own, and at each step, the two classes with the smallest increase in the sum of squared deviations are combined into one class, until all cases are grouped together. The Euclidean distance is used, which tends to cluster the classes with a small number of cases together to find classes of approximately the same size and shape. This method is more effective and widely used.
What are the methods of cluster analysis to measure similarity
Causal measures are the methods of cluster analysis to measure similarity.
Clustering is a technique to find the inherent structure between data. Clustering organizes all data instances into groups of similarities which are called clusters. Data instances in the same cluster are the same as each other, and instances in different clusters are different from each other.
Cluster Analysis Definition
Cluster analysis is the grouping of data objects based on information found in the data that describes the objects and their relationships. The goal is that objects within a group are similar (related) to each other, while objects in different groups are different (unrelated). The greater the similarity within the group and the greater the difference between the groups, the better the clustering.
The effectiveness of clustering depends on two factors: 1. distancemeasurement 2. clustering algorithm
Common Algorithms for Cluster Analysis
K-mean clustering, also known as fast clustering, divides the data into a predetermined number of classes, K, on the basis of a minimization of the error function. The principle of the algorithm is simple and easy to handle large amounts of data K-mean algorithm sensitivity to isolated points, K-centroid algorithm does not use the average value of the objects in the cluster as the center of the cluster, but instead selects the closest object to the mean in the cluster as the center of the cluster.
Also known as hierarchical clustering, the unit of classification from high to low in a tree structure, and the lower the position, the fewer objects it contains, but the more common features between these objects. The clustering method is only suitable for use when the amount of data is small, and the speed will be very slow when the amount of data is large.