Tree-Mining: Understanding Applications and Challenges

Tree-mining is an essential system of techniques and software technologies for multi-level and multi-angled operations in databases. Pertaining to the purview of this manuscript, several applications of various sub-techniques of tree mining have been explored. The current write-up is aimed at investigating the major applications and challenges of different types and techniques of tree mining, as there have been patchy and scanty investigations so far in this context. To accomplish these tasks, the author has reviewed some of the latest and most pertinent research articles of the last two decades to investigate the titled aspects of this technique

Ranking in Distributed Uncertain Database Environments

Distributed data processing is a major field in nowadays applications. Many applications collect and process data from distributed nodes to gain overall results. Large amount of data transfer and network delay made data processing in a centralized manner a hard operation representing an important problem. A very common way to solve this problem is ranking queries. Ranking or top-k queries concentrate only on the highest ranked tuples according to user's interest. Another issue in most nowadays applications is data uncertainty. Many techniques were introduced for modeling, managing, and processing uncertain databases. Although these techniques were efficient, they didn't deal with distributed data uncertainty. This paper deals with both data uncertainty and distribution based on ranking queries. A novel framework is proposed for ranking distributed uncertain data. The framework has a suite of novel algorithms for ranking data and monitoring updates. These algorithms help in reducing the communication rounds used and amount of data transmitted while achieving efficient and effective ranking. Experimental results show that the proposed framework has a great impact in reducing communication cost compared to other techniques.DOI:http://dx.doi.org/10.11591/ijece.v4i4.592

An Integrated MFFP-tree Algorithm for Mining Global Fuzzy Rules from Distributed Databases

Abstract: In the past, many algorithms have been proposed for mining association rules from binary databases. Transactions with quantitative values are, however, also commonly seen in real-world applications. Each transaction in a quantitative database consists of items with their purchased quantities. The multiple fuzzy frequent pattern tree (MFFP-tree) algorithm was thus designed to handle a quantitative database for efficiently mining complete fuzzy frequent itemsets. It however, only processes a database for mining the desired rules. In this paper, we propose an integrated MFFP (called iMFFP)-tree algorithm for merging several individual MFFP trees into an integrated one. The proposed iMFFP-tree algorithm firstly handles the fuzzy regions for providing linguistic knowledge for human beings. The integration mechanism of the proposed algorithm thus efficiently and completely moves a branch from one sub-tree to the integrated tree. The proposed approach can derive both global and local fuzzy rules from distributed databases, thus allowing managers to make more significant and flexible decisions. Experimental results also showed the performance of the proposed approach