Quiz
Descriptive Data Mining
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Questions in this quiz (28)
1[blank_start]Observation[blank_end]: set of recorded values of variables associated with a single entity
- AObservation
2[blank_start]Unsupervised learning[blank_end]—To detect patterns and relationships in the data
- AUnsupervised learning
3[blank_start]Supervised learning[blank_end]—For prediction and classification
- ASupervised learning
4Data is [blank_start]missing not at random[blank_end] if the reason that the value is missing is related to the value of the variable
- Amissing not at random
5If the missing value is a random occurrence, it is called a data value [blank_start]missing completely at random[blank_end]
- Amissing completely at random
6[blank_start]Dimension reduction[blank_end]: Process of removing variables from the analysis without losing any crucial information
- ADimension reduction
7[blank_start]Cluster analysis[blank_end] is to segment observations into similar groups based on observed variable
- ACluster analysis
8[blank_start]k-means clustering[blank_end] assigns each observation to one of k clusters in a manner such that the observations assigned to the same cluster are as similar as possible
- Ak-means clustering
9If the missing values are not completely random (i.e., correlated with the values of some other variables), these are called [blank_start]missing at random[blank_end]
- Amissing at random
10[blank_start]Bottom- up hierarchical clustering[blank_end] starts with each observation belonging to its own cluster and then sequentially merges the most similar clusters to create a series of nested clusters
- ABottom- up hierarchical clustering
11The simplest overlap measure is called the [blank_start]matching coefficient[blank_end]
- Amatching coefficient
12To avoid misstating similarity due to the absence of a feature, a similarity measure called [blank_start]Jaccard’s coefficient[blank_end]
- AJaccard’s coefficient
13[blank_start]Group Average Linkage[blank_end]: Defines the similarity between two clusters to be the average similarity computed over all pairs of observations between the two clusters
- AGroup Average Linkage
14[blank_start]Single linkage[blank_end]: The similarity between two clusters is defined by the similarity of the pair of observations (one from each cluster) that are the most similar
- ASingle linkage
15[blank_start]Complete linkage[blank_end]: This clustering method defines the similarity between two clusters as the similarity of the pair of observations (one from each cluster) that are the most different
- AComplete linkage
16[blank_start]Median Linkage[blank_end]: Analogous to group average linkage except that it uses the median of the similarities computer between all pairs of observations between the two clusters
- AMedian Linkage
17[blank_start]Ward’s method[blank_end] merges two clusters such that the dissimilarity of the observations with the resulting single cluster increases as little as possible
- AWard’s method
18[blank_start]Euclidean distance[blank_end]: most common method to measure dissimilarity between observations.
- AEuclidean distance
19[blank_start]Centroid linkage[blank_end] uses the averaging concept of cluster centroids to define between-cluster similarity
- ACentroid linkage
20When [blank_start]McQuitty’s method[blank_end] considers merging two clusters A and B, the dissimilarity of the resulting cluster AB to any other cluster C is calculate as: ((dissimilarity between A and C) + (dissimilarity between B and C))/2)
- AMcQuitty’s method
21[blank_start]Association rules[blank_end]: if-then statements which convey the likelihood of certain items being purchased together
- AAssociation rules
22Although association rules are an important tool in [blank_start]market basket analysis[blank_end]
- Amarket basket analysis
23A [blank_start]dendrogram[blank_end] is a chart that depicts the set of nested clusters resulting at each step of aggregation
- Adendrogram
24[blank_start]Antecedent[blank_end]: the collection of items (or item set) corresponding to the if portion of the rule
- AAntecedent
25[blank_start]Support count of an item[blank_end]: number of transactions in the data that include that item set
- ASupport count of an item
26[blank_start]Consequent[blank_end]: the item set corresponding to the then portion of the rule
- AConsequent
27[blank_start]Lift ratio[blank_end]: Measure to evaluate the efficiency of a rule
- ALift ratio
28[blank_start]Confidence[blank_end]: Helps identify reliable association rules
- AConfidence
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- Description
- Business Analytics Quiz on Descriptive Data Mining, created by Allona Mae Dapeg on 15/10/2021.
- Author
- Allona Mae Dapeg
- Language
- English
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