Which of the following statements are correct?A. In clustering, objects are grouped together based on the principle of m

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Which of the following statements are correct?A. In clustering, objects are grouped together based on the principle of maximizing the interclass distance and minimizing the intraclass distance.B. Consider a transaction dataset that contains five items, {A,B,C,D,E}. Suppose the support of itemset {A} is the same as the support of itemset {A, C}, then all transactions that contain the item A may not contain the item C.C. For a nearest neighbor classifier if the parameter k is too small, then it may be susceptible to overfitting due to noise.
Which of the following statements are correct?A. In clustering, objects are grouped together based on the principle of maximizing the interclass distance and minimizing the intraclass distance.B. Consider a transaction dataset that contains five items, {A,B,C,D,E}. Suppose the support of itemset {A} is the same as the support of itemset {A, C}, then all transactions that contain the item A may not contain the item C.C. For a nearest neighbor classifier if the parameter k is too small, then it may be susceptible to overfitting due to noise.
Which of the following statements are correct?A. In clustering, objects are grouped together based on the principle of maximizing the interclass distance and minimizing the intraclass distance.B. Consider a transaction dataset that contains five items, {A,B,C,D,E}. Suppose the support of itemset {A} is the same as the support of itemset {A, C}, then all transactions that contain the item A may not contain the item C.C. For a nearest neighbor classifier if the parameter k is too small, then it may be susceptible to overfitting due to noise.
A. In clustering, objects are grouped together based on the principle of maximizing the interclass distance and minimizing the intraclass distance.
B. Consider a transaction dataset that contains five items, {A,B,C,D,E}. Suppose the support of itemset {A} is the same as the support of itemset {A, C}, then all transactions that contain the item A may not contain the item C.
C. For a nearest neighbor classifier if the parameter k is too small, then it may be susceptible to overfitting due to noise.