1 / 3

Questions Review1 October 4, 2011

Questions Review1 October 4, 2011. In the news clustering problem we computed the distance between two news entities based on their (key-) wordlists A and B as follows: distance(A,B)=1-(|A B)|/|AB|) with ‘||’ denoting set cardinality; e.g. |{ a,b }|=2. Why do we divide by (AB) in the formula?

bill
Download Presentation

Questions Review1 October 4, 2011

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Questions Review1 October 4, 2011 • In the news clustering problem we computed the distance between two news entities based on their (key-) wordlists A and B as follows: distance(A,B)=1-(|AB)|/|AB|) with ‘||’ denoting set cardinality; e.g. |{a,b}|=2. Why do we divide by (AB) in the formula? • What is the main difference between ordinal and a nominal attributes? • What role does exploratory data analysis play in a data mining project? • Assume we have a dataset in which the median of the first attribute is twice as large as the mean of the first attribute? What does this tell you about the distribution of the first attribute? • What is (are) the characteristic(s) of a good histogram (for an attribute)? • Assume you find out that two attributes have a correlation of 0.02; what does this tell you about the relationship of the two attributes? Answer the same question assuming the correlation is -0.98! • What of the following cluster shapes K-means is capable to discover? a) triangles b) clusters inside clusters c) the letter ‘T ‘d) any polygon of 5 points e) the letter ’I’ 8. Assume we apply K-medoids for k=3 to a dataset consisting of 5 objects numbered 1,..5 with the following distance matrix: Distance Matrix: 0 2 4 5 1 object1 0 2 3 3 0 1 5 0 2 0 The current set of representatives is {1,3,4}; indicate all computations k-medoids (PAM) performs in its next iteration 9. What are the characteristics of a border point in DBSCAN? • If you increase the MinPts parameter of DBSCAN; how will this affect the clustering results? • DBSCAN supports the notion of outliers. Why is this desirable? • What is the APRIORI property? • Assume the APRIORI algorithm identified the following 6 4-item sets that satisfy a user given support threshold: abcd, acde, acdf, adfg, bcde, and bcdf; what initial candidate 5-itemsets are created by the APRIORI algorithm; which of those survive subset pruning?

  2. A Few Answers Review September 23, 2010 • In the news clustering problem we computed the distance between two news entities based on their (key-) wordlists A and B as follows: distance(A,B)=1-(|AB)|/|AB|) with ‘||’ denoting set cardinality; e.g. |{a,b}|=2. Why do we divide by (AB) in the formula? • What is the main difference between ordinal and a nominal attributes? The values of nominal attributes are ordered; this fact has to be considered when assessing similarity between two attribute values • Name two descriptive data mining methods! • What are the reasons for the current popularity of knowledge discovery in commercial and scientific applications? • Most prediction techniques employ supervised learning approaches. Explain! • What role does exploratory data analysis play in a data mining project? • Assume we have a dataset in which the median of the first attribute is twice as large as the mean of the first attribute? What does this tell you about the distribution of the first attribute? • What is (are) the characteristic(s) of a good histogram (for an attribute)? It captures the most important characteristics of the underlying density function. • Assume you find out that two attributes have a correlation of 0.02; what does this tell you about the relationship of the two attributes? Answer the same question assuming the correlation is -0.98! 0.02:= no linear relation. -0.98:=a strong linear relationship exists—if the value of one attribute goes up the value of the other goes down

  3. More Answers • The decision tree induction algorithm, discussed in class, is a greedy algorithm. Explain! Does not backtrack/change previously made decisions; in general, greedy algorithms center on finding a path from the current state to the goal state, and do not revise the currently taken path from the initial state to the current state. • Compute the Gini-gain for a 3-way split for a 3-class classification problem; the class-distribution before the split is (10, 5, 5) and after the split the class distribution is (0,0,5), (9, 2,0) and (1,3,0). G(1/2,1/4,1/4)-(1/4*0+11/20*G(9/11.2/11,0)+4/20*G(1/4,3/4)) • What is overfitting ? What is underfitting? What can be done to address overfitting/underfitting in decision tree induction? Overfitting: the model is too complex, the training error is very low but the testing error is not minimal. Underfitting: the model is too simple, both training error and testing error are high. • Most decision tree learning tools use gain-ratio and not information gain; why? • Are decision trees suitable for classification problems involving continuous attributes when classes have multi-modal (http://en.wikipedia.org/wiki/Multimodal) distributions? Give reasons for your answer. Yes, because

More Related