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Data Mining II

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36 Questions

What is the main purpose of the K-means algorithm?

To divide a dataset into non-overlapping subgroups or clusters

What is the term for the process of re-calculating the centroids of clusters?

Update

What is used to measure the distance between a data point and a centroid?

Euclidean distance

What is the term for the coefficients that determine the degree of membership of a data point in a cluster?

Membership coefficients

What is the purpose of external criteria in cluster analysis?

To evaluate the goodness of clusters

What is the stopping criterion for the K-means algorithm?

When the maximum number of iterations is reached

What is the primary advantage of using Partitioning Around Medoids (PAM) clustering over K-Means?

It is more robust to outliers.

In K-Means clustering, what is recalculated after assigning data points to the nearest centroid?

The centroids.

Which clustering method allows one piece of data to belong to two or more clusters?

Fuzzy C-Means (FCM).

What is used to represent the center of a cluster in Partitioning Around Medoids (PAM) clustering?

An actual data point called a medoid.

What is minimized in Partitioning Around Medoids (PAM) clustering?

A sum of dissimilarities.

What determines the membership degree in Fuzzy C-Means (FCM) clustering?

The Euclidean distance between the data point and the cluster center.

What does the 'k' in the k-means algorithm represent?

The number of clusters

What is the difference between a cluster and a group in data mining?

There is no difference between a cluster and a group

What type of clustering is characterized by each data point belonging exclusively to one cluster?

Hard Clustering

What is the primary goal of clustering in data mining?

To find the underlying patterns in the data

What is the characteristic of partitional clustering?

Data is divided into non-overlapping subsets

What is the purpose of clustering in data mining?

To identify the underlying patterns in the data

What is the key difference between hard clustering and fuzzy clustering?

In hard clustering, each data point belongs exclusively to one cluster, whereas in fuzzy clustering, each data point can belong to multiple clusters with varying degrees of membership.

What is the primary goal of hierarchical clustering?

To build a hierarchy of clusters.

What is the advantage of hierarchical agglomerative clustering?

It can find smaller clusters in the data that other clustering methods might miss.

What is the main difference between hierarchical agglomerative clustering and hierarchical divisive clustering?

Hierarchical agglomerative clustering is a 'bottom-up' approach, whereas hierarchical divisive clustering is a 'top-down' approach.

What is a characteristic of fuzzy clustering that makes it particularly useful in certain situations?

Its flexibility in dealing with unclear or well-defined boundaries between clusters.

What is the primary output of cluster analysis?

A grouping of data points into clusters based on their similarities and differences.

What is the key characteristic of K-Means clustering that distinguishes it from hierarchical clustering?

It partitions the data into distinct, non-overlapping clusters.

What is the primary goal of clustering in data mining?

To find underlying patterns or groups in the data

How does the initial selection of centroids affect the outcome of K-Means clustering?

The randomly selected initial centroids can lead to different clustering results.

What is the key difference between hard clustering and fuzzy clustering?

In hard clustering, each data point belongs exclusively to one cluster, whereas in fuzzy clustering, a data point can belong to multiple clusters with varying degrees of membership.

What is the primary advantage of using Fuzzy C-Means clustering over hard clustering methods?

It allows for partial membership of a data point in multiple clusters.

How does Partitioning Around Medoids (PAM) clustering differ from K-Means clustering?

PAM uses medoids (actual data points) to represent cluster centers, whereas K-Means uses mean values.

What is the purpose of the k-means algorithm in data mining?

To divide data into non-overlapping subsets (clusters) such that each data object is in exactly one subset.

What is the main purpose of cluster analysis in data mining?

To group similar data points into clusters based on their characteristics.

What is the main characteristic of hierarchical clustering?

Not specified in the given text

What is the role of cluster analysis in data mining?

To identify patterns or groups in the data and understand the underlying structure

What is the key difference between hard clustering and fuzzy clustering?

Hard clustering assigns each data point to a single cluster, while fuzzy clustering allows for partial membership.

What is the difference between a cluster and a group in data mining?

Not specified in the given text

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