Using Tree Structure to Mine High Temporal Fuzzy Utility Itemsets

Abstract

Data mining is a critical technology for extracting valuable knowledge from databases. It has been used in many fields, like retail, finance, biology, etc. In computational intelligence, fuzzy logic has been applied in many intelligent systems widely because it is simple and similar to human inference. Fuzzy utility mining combines utility mining and fuzzy logic for getting linguistic utility knowledge. In this paper, we study a more challenging, complicated, but practical topic called temporal fuzzy utility data mining, which considers the temporal periods in transactions, purchased amounts, item profits, and understandable linguistic terms as important factors. Although an Apriori-based algorithm was proposed previously, its execution was not efficient. We thus use a modified tree structure based on the classical frequent-pattern tree to improve its performance. A tree-based mining algorithm is also proposed to mine temporal fuzzy utility itemsets from quantitative transactional databases. The tree structure is built to keep all temporal fuzzy utility 1-itemsets in a database. All the high temporal fuzzy utility itemsets in a database can be obtained by traversing the treebased structure. The proposed algorithm gets the final results through two phases. In the first phase, a procedure like FP-Growth is used to find the candidate itemsets. In the second phase, the temporal fuzzy utility database is scanned to decide whether the candidate itemsets are desired. Experimental results show that the proposed algorithm is superior to the existing algorithm for temporal fuzzy utility mining in terms of processing time and used memory.INDEX TERMS fuzzy set, quantitative database, temporal fuzzy utility mining, tree structure, utility mining.

Mining Transactional Databases for Frequent and High-Utility Fuzzy Sequential Patterns With Time Intervals

“…FHT-FUP algorithm was presented by Hong et.al. based on an FPtree structure for fuzzy utility mining [32]. This algorithm transformed quantity into linguistic terms and computed utility using maximum utility measure.…”

“…Synthetic data sets are generated by varying parameter values represented as: The average length of the items in a transaction is denoted by the letter T , I represents the maximal potentially, N and D represent the entire number of items and transactions, respectively. The data sets are generated under the T 5.I6.N 4K.D150K, T 5.I6.N 4K.D200K and T 7.I6.N 4K.D300K parameter Experiments on synthetic datasets were performed to evaluate and compare the results of the proposed FFU-TSPM, TPFU [33], and FHTFUP [32] algorithms. TPFU algorithm performs mining based on utility as the threshold.…”

“…Lan et.al. applied the concept of maximum utility and an upper bound model while considering quantities in the fuzzy form [31,32]. The algorithm calculated fuzzy utility by selecting a maximal value from all the fuzzy utilities for a sequence.…”

“…Hong et.al. presented an FHTFUP algorithm based on an FP-tree structure for fuzzy utility mining [32]. FHTFUP algorithm expressed quantity into linguistic terms and then calculated the maximal fuzzy utility value using the maximum measure [22].…”

Pola Beli Konsumen Menggunakan Algoritma Fp-Growth Untuk Rekomendasi Promosi Pada Aneka Jaya Motor

“…FP-Tree dibentuk dengan metode menempatkan tiap data rekaman transaksi ke dalam setiap jalur khusu di dalam FP-Tree, sebab dalam setiap transaksi yang sudah ditempatkan bisa jadi terdapat transaksi lain yang mempunyai item yang serupa, alhasil jalurnya membolehkan untuk saling menimpa. Semakin banyak data transaksi yang mempunyai item yang serupa maka proses pemampatan dengan bentuk data FP-Tree juga menjadi efisien [12], [13]. Pembangunan FP-Tree dibagi menjadi tiga langkah utama sebagai berikut : a. Melakukan scanning pada sekumpulan data untuk memastikan jumlah support dari masingmasing item.…”