@techreport{oai:ipsj.ixsq.nii.ac.jp:00220225,
 author = {川上, 健太郎 and 栗原, 康志 and 山田, 芙夕楓 and 田原, 司睦},
 issue = {3},
 month = {Sep},
 note = {This paper reports on our study on speeding up the pattern mining processing for matrices whose elements are binary values of 0 or 1. For example, assume that a matrix X consisting of I rows and J columns represents the purchasing history of J persons for I products. The (i, j) component of the matrix X is set to 1 if j-th person purchased i-th product and 0 otherwise. If we want to determine whether L products are likely to be purchased by the same person, we can calculate by data mining the matrix X. The computational complexity of this processing is O(J・L・I CL), which is proportional to the L-th power of I and grows almost exponentially with L. In this paper, we examine the order of computation so that the process can be performed in realistic time for larger I, J, and L. In particular, thread parallelism and L1/L2 cache efficiency are considered., This paper reports on our study on speeding up the pattern mining processing for matrices whose elements are binary values of 0 or 1. For example, assume that a matrix X consisting of I rows and J columns represents the purchasing history of J persons for I products. The (i, j) component of the matrix X is set to 1 if j-th person purchased i-th product and 0 otherwise. If we want to determine whether L products are likely to be purchased by the same person, we can calculate by data mining the matrix X. The computational complexity of this processing is O(J・L・I CL), which is proportional to the L-th power of I and grows almost exponentially with L. In this paper, we examine the order of computation so that the process can be performed in realistic time for larger I, J, and L. In particular, thread parallelism and L1/L2 cache efficiency are considered.},
 title = {2値行列に対するパターンマイニング処理の高速化の検討},
 year = {2022}
}