103,086 research outputs found

    Parallel Frequent Item Set Mining with Selective Item Replication

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    Cataloged from PDF version of article.We introduce a transaction database distribution scheme that divides the frequent item set mining task in a top-down fashion. Our method operates on a graph where vertices correspond to frequent items and edges correspond to frequent item sets of size two. We show that partitioning this graph by a vertex separator is sufficient to decide a distribution of the items such that the subdatabases determined by the item distribution can be mined independently. This distribution entails an amount of data replication, which may be reduced by setting appropriate weights to vertices. The data distribution scheme is used in the design of two new parallel frequent item set mining algorithms. Both algorithms replicate the items that correspond to the separator. NoClique replicates the work induced by the separator and NoClique2 computes the same work collectively. Computational load balancing and minimization of redundant or collective work may be achieved by assigning appropriate load estimates to vertices. The experiments show favorable speedups on a system with small-to-medium number of processors for synthetic and real-world databases

    Frequent Item Set Mining In Data Mining: A survey

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    Data mining is process of extracting useful information from different perspectives. Frequent Item set mining is widely used in financial, retail and telecommunication industry. The major concern of these industries is faster processing of a very large amount of data. Frequent item sets are those items which are frequently occurred. So we can use different types of algorithms for this purpose. Frequent Itemset mining can be performed Apriori, FP-tree, Eclat, and RARM algorithms. For the work in this paper, we have analyzed widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori, Frequent Pattern (FP) Growth, Rapid Association Rule Mining (RARM) and ECLAT algorithm frequent pattern mining algorithms. This study also focuses on each of the algorithm’s advantages, disadvantages and limitations for finding patterns among large item sets in database systems

    A Hash Based Frequent Item set Mining using Rehashing

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    Data mining is the use of automated data analysis techniques to uncover previously undetected relationships among data items. Mining frequent item sets is one of the most important concepts of data mining. Frequent item set mining has been a highly concerned field of data mining for researcher for over two decades. It plays an essential role in many data mining tasks that try to find interesting itemsets from databases, such as association rules, correlations, sequences, classifiers and clusters . In this paper, we propose a new association rule mining algorithm called Rehashing Based Frequent Item set (RBFI) in which hashing technology is used to store the database in vertical data format. To avoid hash collision and secondary clustering problem in hashing, rehashing technique is utilized here. The advantages of this new hashing technique are easy to compute the hash function, fast access of data and efficiency. This algorithm provides facilities to avoid unnecessary scans to the database

    Mining Frequent Item Sets Data Streams using "ÉclatAlgorithm"

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    Frequent pattern mining is the process of mining data in a set of items or some patterns from a largedatabase. The resulted frequent set data supports the minimum support threshold. A frequentpattern is a pattern that occurs frequently in a dataset. Association rule mining is defined as to findout association rules that satisfy the predefined minimum support and confidence from a given database. If an item set is said to be frequent, that item set supports the minimum support andconfidence. A Frequent item set should appear in all the transaction of that data base. Discoveringfrequent item sets play a very important role in mining association rules, sequence rules, web logmining and many other interesting patterns among complex data. Data stream is a real timecontinuous, ordered sequence of items. It is an uninterrupted flow of a long sequence of data. Somereal time examples of data stream data are sensor network data, telecommunication data,transactional data and scientific surveillances systems. These data produced trillions of updatesevery day. So it is very difficult to store the entire data. In that time some mining process is required.Data mining is the non-trivial process of identifying valid, original, potentially useful and ultimatelyunderstandable patterns in data. It is an extraction of the hidden predictive information from largedata base. There are lots of algorithms used to find out the frequent item set. In that Apriorialgorithm is the very first classical algorithm used to find the frequent item set. Apart from Apriori,lots of algorithms generated but they are similar to Apriori. They are based on prune and candidategeneration. It takes more memory and time to find out the frequent item set. In this paper, we havestudied about how the éclat algorithm is used in data streams to find out the frequent item sets.Éclat algorithm need not required candidate generation

    Analisis Keranjang Belanja dengan Algoritma Apriori Klasik pada Data Mining

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    Association Rule Mining is an area of data mining that focus on pruning candidate keys, to find frequent item set. For example, a set of items, such as milk and bread, that appear frequently together in a transaction data set is a frequent itemset. A subsequence, such as buying first PC, then a digital camera, and then a memory card, if it occurs frequently in a shopping history database, is a (frequent) sequential pattern, also knwon as market basket analysis. This paper describes the step by step classical apriori on market basket analysis. Keywords: apriori algorithm, frequent item set, market basket analysis, association rule Abstrak Penambangan Aturan Asosiasi adalah area data mining yang fokus pada pemangkasan kunci kandidat, untuk menemukan frequent itemset. Sebagai contoh, satu set item, misalnya susu dan roti, yang muncul sering bersama-sama di set data transaksi adalah frequent itemset. Berikutnya, pelanggan, misalnya membeli PC dahulu, lalu kamera digital, lalu kartu memori, jika ini sering terjadi dalam riwayat basisdata belanja, adalah pola sekuensial berurutan (sering), juga dikenal sebagai analisis keranjang belanja. Tulisan ini menjelaskan langkah demi langkah algoritma apriori klasik pada analisis keranjang belanja. Kata kunci: algoritma apriori, frequent itemset, analisis keranjang belanja, aturan asosias
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