Data Mining: Implementation of Data Mining Techniques using RapidMiner software
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K-means and k-medoids clustering techniques are illustrated using RapidMiner tool and a Java application. K-means partitions data into k groups based on minimizing distance between data points and cluster centers. It assigns each data point to exactly one cluster. K-medoids is similar but uses actual data points as centers instead of means. Both require specifying the number of clusters k in advance and can be impacted by outliers, though k-medoids is less sensitive to outliers. The document demonstrates implementing both techniques using different software and compares the results.
Data Mining: Implementation of Data Mining Techniques using RapidMiner software
- 1. Data Mining: Implementation of Data Mining Techniques using RapidMiner software Prepared by Mohammed Kharma
- 2. Definitions review • Cluster: A collection of data objects – similar (or related) to one another within the same group – dissimilar (or unrelated) to the objects in other groups • Cluster analysis – Finding similarities between data according to the characteristics found in the data and grouping similar data objects into clusters
- 3. Clustering Methods • Partitioning : – Unsupervised learning algorithms, Construct various partitions and then evaluate them by some criterion, e.g., minimizing the sum of square errors – Typical methods: k-means, k-medoids • Hierarchical : – Create a hierarchical decomposition of the set of data (or objects) using some criterion – Typical methods: Diana, Agnes, BIRCH, ROCK, CAMELEON
- 4. Illustration & compression of 2 clustering technique using Rapidminer tool and Java application
- 5. illustrate of 2 clustering technique using Rapidminer tool and Java • K-means algorithm: We performed two test 1. Using java program: program parameters K = 2; Data: 22 21 19 20 18 22 1 3 3 2
- 6. 6 K-means Clustering • Input: the number of clusters K and the collection of n instances • Output: a set of k clusters that minimizes the squared error criterion • Method: – Arbitrarily choose k instances as the initial cluster centers – Repeat • (Re)assign each instance to the cluster to which the instance is the most similar, based on the mean value of the instances in the cluster • Update cluster means (compute mean value of the instances for each cluster) – Until no change in the assignment • Squared Error Criterion – E = ∑i=1 k ∑ pЄCi |p-mi|2 – where mi are the cluster means and p are points in clusters
- 7. The result K-Means-java program
- 8. The result of K-Means-RapidMiner
- 9. The result of K-Means-RapidMiner
- 10. Continued-The result of K-Means- RapidMiner
- 11. 11 K-medoids • Input: the number of clusters K and the collection of n instances • Output: A set of k clusters that minimizes the sum of the dissimilarities of all the instances to their nearest medoids • Method: – Arbitrarily choose k instances as the initial medoids – Repeat • (Re)assign each remaining instance to the cluster with the nearest medoid • Randomly select a non-medoid instance, or • Compute the total cost, S, of swapping Oj with Or • If S<0 then swap Oj with Or to form the new set of k medoids – Until no change
- 12. The result of k-medoids-RapidMiner
- 13. The result of k-medoids-RapidMiner
- 15. Comparison The results of both algorithms are the same Both require K to be specified in the input K-medoids is less influenced by outliers in the data Both methods assign each instance exactly to one cluster
- 16. »Thank you