Data Mining II
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Questions and Answers

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

  • To select the best number of clusters in a dataset
  • To determine the validity indices of clusters
  • To calculate the Euclidean distance between data points
  • To divide a dataset into non-overlapping subgroups or clusters (correct)
  • What is the term for the process of re-calculating the centroids of clusters?

  • Iteration
  • Initialization
  • Assignment
  • Update (correct)
  • What is used to measure the distance between a data point and a centroid?

  • Euclidean distance (correct)
  • Membership coefficient
  • Validity index
  • Cluster coefficient
  • What is the term for the coefficients that determine the degree of membership of a data point in a cluster?

    <p>Membership coefficients</p> Signup and view all the answers

    What is the purpose of external criteria in cluster analysis?

    <p>To evaluate the goodness of clusters</p> Signup and view all the answers

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

    <p>When the maximum number of iterations is reached</p> Signup and view all the answers

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

    <p>It is more robust to outliers.</p> Signup and view all the answers

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

    <p>The centroids.</p> Signup and view all the answers

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

    <p>Fuzzy C-Means (FCM).</p> Signup and view all the answers

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

    <p>An actual data point called a medoid.</p> Signup and view all the answers

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

    <p>A sum of dissimilarities.</p> Signup and view all the answers

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

    <p>The Euclidean distance between the data point and the cluster center.</p> Signup and view all the answers

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

    <p>The number of clusters</p> Signup and view all the answers

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

    <p>There is no difference between a cluster and a group</p> Signup and view all the answers

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

    <p>Hard Clustering</p> Signup and view all the answers

    What is the primary goal of clustering in data mining?

    <p>To find the underlying patterns in the data</p> Signup and view all the answers

    What is the characteristic of partitional clustering?

    <p>Data is divided into non-overlapping subsets</p> Signup and view all the answers

    What is the purpose of clustering in data mining?

    <p>To identify the underlying patterns in the data</p> Signup and view all the answers

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

    <p>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.</p> Signup and view all the answers

    What is the primary goal of hierarchical clustering?

    <p>To build a hierarchy of clusters.</p> Signup and view all the answers

    What is the advantage of hierarchical agglomerative clustering?

    <p>It can find smaller clusters in the data that other clustering methods might miss.</p> Signup and view all the answers

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

    <p>Hierarchical agglomerative clustering is a 'bottom-up' approach, whereas hierarchical divisive clustering is a 'top-down' approach.</p> Signup and view all the answers

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

    <p>Its flexibility in dealing with unclear or well-defined boundaries between clusters.</p> Signup and view all the answers

    What is the primary output of cluster analysis?

    <p>A grouping of data points into clusters based on their similarities and differences.</p> Signup and view all the answers

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

    <p>It partitions the data into distinct, non-overlapping clusters.</p> Signup and view all the answers

    What is the primary goal of clustering in data mining?

    <p>To find underlying patterns or groups in the data</p> Signup and view all the answers

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

    <p>The randomly selected initial centroids can lead to different clustering results.</p> Signup and view all the answers

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

    <p>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.</p> Signup and view all the answers

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

    <p>It allows for partial membership of a data point in multiple clusters.</p> Signup and view all the answers

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

    <p>PAM uses medoids (actual data points) to represent cluster centers, whereas K-Means uses mean values.</p> Signup and view all the answers

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

    <p>To divide data into non-overlapping subsets (clusters) such that each data object is in exactly one subset.</p> Signup and view all the answers

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

    <p>To group similar data points into clusters based on their characteristics.</p> Signup and view all the answers

    What is the main characteristic of hierarchical clustering?

    <p>Not specified in the given text</p> Signup and view all the answers

    What is the role of cluster analysis in data mining?

    <p>To identify patterns or groups in the data and understand the underlying structure</p> Signup and view all the answers

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

    <p>Hard clustering assigns each data point to a single cluster, while fuzzy clustering allows for partial membership.</p> Signup and view all the answers

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

    <p>Not specified in the given text</p> Signup and view all the answers

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