Analisis Keranjang Belanja dengan Algoritma Apriori Klasik pada Data Mining

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 asosiasi

MINING TOP-K FREQUENT SEQUENTIAL PATTERN IN ITEM INTERVAL EXTENDED SEQUENCE DATABASE

Frequent sequential pattern mining in item interval extended sequence database (iSDB) has been one of interesting task in recent years. Unlike classic frequent sequential pattern mining, the pattern mining in iSDB also consider the item interval between successive items; thus, it may extract more meaningful sequential patterns in real life. Most previous frequent sequential pattern mining in iSDB algorithms needs a minimum support threshold (minsup) to perform the mining. However, it’s not easy for users to provide an appropriate threshold in practice. The too high minsup value will lead to missing valuable patterns, while the too low minsup value may generate too many useless patterns. To address this problem, we propose an algorithm: TopKWFP – Top-k weighted frequent sequential pattern mining in item interval extended sequence database. Our algorithm doesn’t need to provide a fixed minsup value, this minsup value will dynamically raise during the mining process

A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences

Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method.