Fowlkes–Mallows index

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Fowlkes–Mallows index[1] is an external evaluation method that is used to determine the similarity between two clusterings (clusters obtained after a clustering algorithm). This measure of similarity could be either between two hierarchical clusterings or a clustering and a benchmark classification. A higher value for the Fowlkes–Mallows index indicates a greater similarity between the clusters and the benchmark classifications.

Preliminaries[edit]

The Fowlkes–Mallows index, when results of two clustering algorithms is used to evaluate the results, is defined as[2]


FM = \sqrt{ \frac {TP}{TP+FP} \cdot \frac{TP}{TP+FN}  }
where TP is the number of true positives, FP is the number of false positives, and FN is the number of false negatives.

Definition[edit]

Consider two hierarchical clusterings of n objects labeled A_1 and A_2. The trees A_1 and A_2 can be cut to produce k=2,\ldots,n-1 clusters for each tree (by either selecting clusters at a particular height of the tree or setting different strength of the hierarchical clustering). For each value of k, the following table can then be created

M=[m_{i,j}]  \qquad   (i=1,\ldots,k \text{ and } j=1,\ldots,k)

where m_{i,j} is of objects common between the ith cluster of A_1 and jth cluster of A_2. The Fowlkes–Mallows index for the specific value of k is then defined as

B_k=\frac{T_k}{\sqrt{P_kQ_k}}

where

T_k=\sum_{i=1}^{k}\sum_{j=1}^{k}m_{i,j}^2-n
P_k=\sum_{i=1}^{k}(\sum_{j=1}^{k}m_{i,j})^2-n
Q_k=\sum_{j=1}^{k}(\sum_{i=1}^{k}m_{i,j})^2-n

B_k can then be calculated for every value of k and the similarity between the two clusterings can be shown by plotting B_k versus k. For each k we have 0 \le B_k \le 1.

Fowlkes–Mallows index can also be defined based on the number of points that are common or uncommon in the two hierarchical clusterings. If we define

TP as the number of points that are present in the same cluster in both A_1 and A_2.
FP as the number of points that are present in the same cluster in A_1 but not in A_2.
FN as the number of points that are present in the same cluster in A_2 but not in A_1.
TN as the number of points that are in different clusters in both A_1 and A_2.

It can be shown that the four counts have the following property


TP+FP+FN+TN=n(n-1)/2

and that the Fowlkes–Mallows index for two clusterings can be defined as[3]


FM = \sqrt{ \frac {TP}{TP+FP} \cdot \frac{TP}{TP+FN}  }
where TP is the number of true positives, FP is the number of false positives, and FN is the number of false negatives.

Discussion[edit]

Since the index is directly proportional to the number of true positives, a higher index means greater similarity between the two clusterings used to determine the index. One of the most basic thing to test the validity of this index is to compare two clusterings that are unrelated to each other. Fowlkes and Mallows showed that on using two unrelated clusterings, the value of this index approaches zero as the number of total data points chosen for clustering increase; whereas the value for the Rand index for the same data quickly approaches 1[1] making Fowlkes–Mallows index a much accurate representation for unrelated data. This index also performs well if noise is added to an existing dataset and their similarity compared. Fowlkes and Mallows showed that the value of the index decreases as the component of the noise increases. The index also showed similarity even when the noisy dataset had different number of clusters than the clusters of the original dataset. Thus making it a reliable tool for measuring similarity between two clusters.

References[edit]

  1. ^ a b Fowlkes, E. B.; Mallows, C. L. (1 September 1983). "A Method for Comparing Two Hierarchical Clusterings". Journal of the American Statistical Association 78 (383): 553. doi:10.2307/2288117. 
  2. ^ Halkidi, Maria; Batistakis, Yannis; Vazirgiannis, Michalis (1 January 2001). "On Clustering Validation Techniques". Journal of Intelligent Information Systems 17 (2/3): 107–145. doi:10.1023/A:1012801612483. 
  3. ^ MEILA, M (1 May 2007). "Comparing clusterings—an information based distance". Journal of Multivariate Analysis 98 (5): 873–895. doi:10.1016/j.jmva.2006.11.013. 

Further reading[edit]

  • Ramirez, E. H.; Brena, R.; Magatti, D.; Stella, F. (2010). "Probabilistic Metrics for Soft-Clustering and Topic Model Validation". 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. p. 406. doi:10.1109/WI-IAT.2010.148. ISBN 978-1-4244-8482-9.  edit