The tf-idf algorithm is a method for weighting co-occurrence matrices in information retrieval, usually used when the dimensions are documents. The algorithm is the product of two terms: - Term frequency is the frequency of the word $t$ in the document $d$, commonly denoted in log weighting as $tf_{t, d} = log_{10}(count(t, d) + 1)$. - Inverse document frequency is also usually denoted in a log function as $idf_t = log_{10}\left(\frac{N}{df_t}\right)$, where document frequency $df_t$ of a term $t$ is the number of documents in which it occurs and $N$ is the total number of documents in the collection. The tf-idf weighted value $w_{t, d}$ for word $t$ in document $d$ is thus defined as $w_{t, d} = tf_{t, d} \times idf_t$