Assignment 16 Clustering Algorithms

Which statement best describes k-means cluster analysis? It is the process of estimating the value of a continuous outcome variable. It is the process of organizing observations into distinct groups based on a measure of similarity or dissimilarity. It is the process of reducing the number of variables to consider in data-mining. It is the process of agglomerating observations into a series of nested groups based on a measure of similarity or dissimilarity.

Question: If there are n observations in the data set, after how many iterations will the hierarchical clustering algorithm terminate? • You can start by using some examples to come up with an initial guess, but then see if you can find a general answer and proof of that answer.

Explain how random forest is used for similarity measure.

List, BST (binary search trees), and AVL each have their own unique search time challenges; please describe how each one obtains its findings.

In clustering Select one: O a. it is not possible to use cross-validation to select a good number of clusters. b. in some cases we can validate with data to determine number of clusters. C. the objective is to reduce dimensionality of the data. O d. it is possible to use cross-validation to select a good number of clusters.

Traditional clustering methods are rigid in that they require each object to belong exclusively to only one cluster. Explain why this is a special case of fuzzy clustering. You may use k-means as an example.

Explain "matching in algorithms."

Differentiate between k-means and k-median clustering algorithm.

K-means clustering is run on some data, for a few different values of K. Shown is the plot obtained for the inertia (a measure of average distance of points from the centroid of their allocated cluster), versus the number of clusters. What is the implied natural number of clusters to use? Inertia O 2000 1750 1500 1250 1000 750 500 250 T 9 3 1 -2 3 4 5 6 Number of clusters 7 .8 9

You have hypothetically obtained access to a database listing the locations (in x, y coordinates) and names of all nearby restaurants. Only the following kind of questions are required: The question seeks to determine whether the given location is suitable for an eating establishment. I was wondering if you could explain why R-tree indexing was better than B-tree. Why?

You have access to a database that contains the x, y coordinates and names of all surrounding dining establishments. The only questions that need answering are these: A restaurant's viability at the given location is being questioned. Is it preferable to use a B-tree or an R-tree indexing method, and why? Why?

Task: Apply the Apriori Algorithm for this Data: 1) Min.Support=1 2) Give the state of this rule (Approximate Rule or Rule Exact) A → B ID Items 100 ABCD AD 200 300 BCD 400 ABF 500 AF CF 600 700 ABD

algorithm for Hash function H defined from compression function h via the MD paradigm, andadversary

2) Given a Clustering task, how you can evaluate the performance on the test set and how wewould know if the clusters are correct. Explain any three possible solutions.

Wrong Answers will downvote Cse    Q) Give a brief introduction about GKS.

In order to solve the issue of the Minimum Spanning Tree, what is the most effective approach to use Prim's method?... Do any of the other data structures have an advantage over the others? Why? Explain.

You are getting continuous feeds from stock market. You need to develop an algorithm for real-time clustering of stock patterns. Develop an algorithm and explain it. Can you use incremental learning here for cluster refinement? Which distance measure would you use for this clustering? Why? Explain overfitting and underfitting in above case. What are possible issues if you use Manhattan distance-based clustering in above case?

Design sort-based and hash-based algorithms for computing the relational division operation (see Practice Exercise 2.9 for a definition of the division operation).

Compare and contrast the performance of Binary Search Trees and AVL Trees. Is it really feasible to have a conversation on how to operate a firm more effectively?

An Investigation of the Effect of Unbalanced labeled Data on CM and GRFM Algorithms.

Describe briefly about clustering and Explain K-Nearest Neighbor learning clustering with example. a. Discuss in detail about Locally weighted regression. b. Write the Remarks on locally weighted regression in brief.

Data Mining It is known that there are two point dimensions as shown in Table 2 below [Table attached below] By using the Hierarchical Clustering algorithm, please create a Cluster and a Dendogram and the steps (intermediary 2 matrix) in making the Dendogram using: Average Link. To measure distance please use Euclidean distance.

y 3 y 1 1 4 2 4 5 4 5 2 3 1 5 4 3 5 6 3 Perform Fuzzy Clustering Using the EM Algorithm with custom data with 20 records and 4 clusters.

Please explain b) What are the differences between hash table and balanced binary search trees?

Why and how do local minima affect k-means clustering? What are our options for dealing with these?

Please provide an explanation of the two most significant benefits that come from using binary search trees (referenced based approach).

In Hierarchical clustering, the major difference between the Ward's method and other linkage method is that a. Ward's method uses error sum of squares to measure the loss of information that occurs when cluster are created. b. Ward's method minimizes MAD to minimize the dissimilarity between clusters c. Ward's method uses minimum distance while other linkage methods uses maximum distance between the data points Clear my choice