Clustering: Large Databases in data mining
Download as PPT, PDF5 likes9,132 views
The document discusses different approaches for clustering large databases, including divide-and-conquer, incremental, and parallel clustering. It describes three major scalable clustering algorithms: BIRCH, which incrementally clusters incoming records and organizes clusters in a tree structure; CURE, which uses a divide-and-conquer approach to partition data and cluster subsets independently; and DBSCAN, a density-based algorithm that groups together densely populated areas of points.
![Requirement—Clustering for large database No more (preferably less) than one scan of the database. Process each [record] only once With limited memory f(x): O(n*n). f(x): O(n). Time/ Memory Data Time/ Memory Data Method ??? PP134 Can suspend, stop, and resume Can update the results when new data inserted or removed Can perform different technology to scan the database During execution, method should provide status and ‘best’ answer.](https://image.slidesharecdn.com/clustering-large-databases-in-data-mining-1201505332698108-3/85/Clustering-Large-Databases-in-data-mining-5-320.jpg)
Ad
More Related Content
What's hot (20)
Viewers also liked (20)
Ad
Similar to Clustering: Large Databases in data mining (20)
Ad
More from ZHAO Sam (8)
Recently uploaded (20)
Clustering: Large Databases in data mining
- 1. Chapter 12 Clustering: Large Databases Written by Farial Shahnaz Presented by Zhao Xinyou Data Mining Technology
- 2. Contents Introduction Idea for there major approaches for scalable clustering {Divide-and-Conquer, Incremental, Parallel} There approaches for scalable clustering { BIRCH, DSBCAN, CURE} Application
- 3. Introduction –Common method Common method for clustering: visit all data from database and analyze the data, just like: Time : Computational Complexities: O(n*n). Memory : Need to load all data to main memory PP133 huge, huge number millions Time/ Memory Data
- 4. Motivation—Clustering for large database f(x): O(n*n). f(x): O(n). Time/ Memory Data Time/ Memory Data Method ??? PP134
- 5. Requirement—Clustering for large database No more (preferably less) than one scan of the database. Process each [record] only once With limited memory f(x): O(n*n). f(x): O(n). Time/ Memory Data Time/ Memory Data Method ??? PP134 Can suspend, stop, and resume Can update the results when new data inserted or removed Can perform different technology to scan the database During execution, method should provide status and ‘best’ answer.
- 6. Major approach for scalable clustering Divide-and-Conquer approach Parallel clustering approach Incremental clustering approach PP135
- 7. Divide-and Conquer approach Definition. Divide-and-conquer is a problem-solving approach in which we: divide the problem into sub-problems, recursively conquer or solve each sub-problem, and then combine the sub-problem solutions to obtain a solution to the original problem. PP135 Key Assumptions 1.Problem solutions can be constructed using subproblem solutions. 2.Subproblem solutions are independent of one another. 9*9 数独
- 8. Parallel clustering approach Idea: Divide data into small set and then run small set on different machine (Come from Divide-and-Conquer) PP136-137
- 9. Explanation about Divide-and-Conquer Divide is some algorithms Conquer is some algorithms
- 10. Application Sorting: quick-sort and merge sort Fast Fourier transforms Tower of Hanoi puzzle matrix multiplication ….. PP135
- 11. CURE- Divide-and-Conquer 1.Get the size n of set D and partition D into p group (contain n/p elements) 2.To each group pi, clustered into k groups by using Heap and k-d tree 3.delete some no relationship node in Heap and k-d tree 4. Cluster the partial clusters and get the final cluster PP140-141
- 12. Heap PP140-141
- 13. k-D Tree Technically, the letter k refers to the number of dimensions PP140-141 3-dimensional k d-tree
- 15. CURE- Divide-and-Conquer PP140-141 Nearest Merge Nearest Merge
- 16. Incremental clustering approach Idea: scan all data in database, Compare with the existing clusters, if find similar cluster, assign it to with cluster, or else, create a new cluster. Go on till no data Steps: 1. S={};//set cluster = NULL 2. do{ 3. read one record d; 4. r = find_simiarity_cluster(d, S); 5. if (r exists) 6. assign d to the cluster r 6. else 7. Add_cluster(d, S); 8. } untill (no record in database); PP135-136
- 17. Application--Incremental clustering approach BIRCH Balanced Iterative Reducing and Clustering using Hierarchies DBSCAN Density-Based Spatial Clustering of Application with Noise
- 18. BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies ) Based on distance measurement, compute the similarity between record and cluster and give the clusters. Inner Cluster Among Cluster PP137-138
- 19. BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies ) Inner Cluster Among Cluster PP137-138
- 20. Related Definiation Cluster: {x i }, where i = 1, 2, …, N CF(Clustering Feature) ： is a triple, (N,LS,SS) ， N ： number of data ； LS ： linear sum of N data ； SS ： Square sum
- 21. Related Definiation CF tree = (B,T), B = (CF i , child i ), if is internal node in a cluster B = (CF i , prev, next) if is external or leaf node in a cluster. T: threshold for all leaf node, which should satisfy mean distance D < T
- 23. DBSCAN DBSCAN: Density-Based Spatial Clustering of Application with Noise Ex1: We want to class house along with river from one spatial photo Ex2:
- 24. Definition for DBSCAN Eps-neighborhood of a point The Eps-neighborhood of a point p, denoted by N Eps (p), is defined by N Eps (p)={q ∈D |dist(p,q) ≤ Eps} Minimum Number (MinPts) The MinPts is the minimum number of data points in any cluster.
- 25. Definition for DBSCAN Directly density-reachable A point p is directly density-reachable from a point q. Eps and MinPts if 1): p ∈ N Eps (q); 2): |N Eps (q)| ≥MinPts ;
- 26. Definition for DBSCAN Density-reachable A point p is density-reachable from a point q. Eps and MinPts if there is a chain of points p 1 ,p 2 ,…,p n ,p=p 1 ,q=p n such as p i +1 is directly desity-reachable from p i ;
- 27. Definition for DBSCAN Density-reachable A point p is density-reachable from a point q. Eps and MinPts if there is a chain of points p 1 ,p 2 ,…,p n ,p=p 1 ,q=p n such as p i +1 is directly desity-reachable from p i ;
- 28. Algorithm of DBSCAN Input D={t 1 ,t 2 ,…,t n } MinPts Eps Output K=K 1 ,K 2 ,…K k k = 0; for i =1 to n do if t i is not in a cluster then X={t i | t j is density-reachable from t i } end if if X is a valid cluster then k= k+1; K k = X; end if end for