e119ead869b2fb015502ba4defc5b2f4.zip - Machine learning is a field of computer science that gives computer systems the ability to "learn" (i.e. progressively improve performance on a specific task) with data, without being explicitly programmed.,2018-03-06 20:44:33,下载1次
ijcsit2015060644.zip - Assume that a large supermarket tracks sales data by stock-keeping unit (SKU) for each item: each item, such as "butter" or "bread", is identified by a numerical SKU. The supermarket has a database of transactions where each transaction is a set of SKUs that were bought together.
Let the database of transactions consist of following itemsets:,2018-03-06 20:41:49,下载1次
V3I1-1254.zip - The pseudo code for the algorithm is given below for a transaction database {\displaystyle T} T, and a support threshold of {\displaystyle \epsilon } \epsilon . Usual set theoretic notation is employed, though note that {\displaystyle T} T is a multiset. {\displaystyle C_{k}} C_{k} is the candidate set for level {\displaystyle k} k. At each step, the algorithm is assumed to generate the candidate sets from the large item sets of the preceding level, heeding the downward closure lemma. {\displaystyle count[c]} count[c] accesses a field of the data structure that represents candidate set {\displaystyle c} c, which is initially assumed to be zero. Many details are omitted below, usually the most important part of the implementation is the data structure used for storing the candidate sets, and counting their frequencies.,2018-03-06 20:40:10,下载1次
IRJET-V4I5669.zip - The Apriori algorithm was proposed by Agrawal and Srikant in 1994. Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation). Other algorithms are designed for finding association rules in data having no transactions (Winepi and Minepi), or having no timestamps (DNA sequencing). Each transaction is seen as a set of items (an itemset). Given a threshold {\displaystyle C} C, the Apriori algorithm identifies the item sets which are subsets of at least {\displaystyle C} C transactions in the database.,2018-03-06 20:38:24,下载1次
ajss-3-2-3.zip - Apriori[1] is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis.,2018-03-06 20:37:28,下载2次
AprioriAll-Algorithm-master.zip - To reduce the generation of candidate sequences and the scans to sequence database for
AprioriAll algorithm, an efficient sequential pattern mining method based on improved AprioriAll
algorithm is presented. Firstly, data are preprocessed. Then do the sequential pattern mining with
improved AprioriAll algorithm. The improvements of AprioriAll algorithm are mainly two points:
one is to change the connection of candidate sequences to reduce the generation of candidate
sequences; the other is to reduce the needless database scans to improve the efficiency of
algorithm. Finally, the efficiency and validity of improved AprioriAll algorithm is validated
through experiments.,2018-03-06 20:32:04,下载1次