A review of associative classification mining

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper

A Novel Approach for Finding Rare Items Based on Multiple Minimum Support Framework

AbstractPattern mining methods describe valuable and advantageous items from a large amount of records stored in the corporate datasets and repositories. While mining, literature has almost singularly focused on frequent itemset but in many applications rare ones are of higher interest. For Example medical dataset can be considered, where rare combination of prodrome plays a vital role for the physicians. As rare items contain worthwhile information, researchers are making efforts to examine effective methodologies to extract the same. In this paper, an effort is made to analyze the complete set of rare items for finding almost all possible rare association rules from the dataset. The Proposed approach makes use of Maximum constraint model for extracting the rare items. A new approach is efficient to mine rare association rules which can be defined as rules containing the rare items. Based on the study of relevant data structures of the mining space, this approach utilizes a tree structure to ascertain the rare items. Finally, it is demonstrated that this new approach is more virtuous and robust than the existing algorithms

Multi-threaded Implementation of Association Rule Mining with Visualization of the Pattern Tree

Motor Vehicle fatalities per 100,000 population in the United States has been reported to be 10.69% in the year 2012 as per NHTSA (National Highway Traffic Safety Administration). The fatality rate has increased by 0.27% in 2012 compared to the rate in the year 2011. As per the reports, there are many factors involved in increasing the fatality rate drastically such as driving under influence, testing while driving, and various other weather phenomena. Decision makers need to analyze the factors attributing to the increase in an accident rate to take implied measures. Current methods used to perform the data analysis process has to be reformed and optimized to make policies for controlling the high traffic accident rates. This research work is an extension to the data-mining algorithm implementation Most Associated Sequential Pattern (MASP). MASP uses association rule mining approach to mine interesting traffic accident data using a modified version of FP-growth algorithm. Owing to the huge amounts of available traffic accident data, MASP algorithm needs to be further modified to make it more efficient with respect to both space and time. Therefore, we present a parallel implementation to the MASP algorithm. In addition to this, pattern tree and apriori-tid algorithm implementation has been done. The application is designed in C# using .NET Framework and C# Task Parallel Library

Survey On Moving Towards Frequent Pattern Growth for Infrequent Weighted Itemset Mining

In data mining and knowledge discovery technique domain, frequent pattern mining plays an important role but it does not consider different weight value of the items. Association Rule Mining is to find the correlation between data. The frequent itemsets are patterns or items like itemsets, substructures, or subsequences that come out in a data set frequently or continuously. In this paper we are presenting survey of various frequent pattern mining and weighted itemset mining. Different articles related to frequent and weighted infrequent itemset mining were proposed. This paper focus on survey of various Existing Algorithms related to frequent and infrequent itemset mining which creates a path for future researches in the field of Association Rule Mining

Comparison of different algorithms for exploting the hidden trends in data sources

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2003Includes bibliographical references (leaves: 92-97)Text in English; Abstract: Turkish and English97 leavesThe growth of large-scale transactional databases, time-series databases and other kinds of databases has been giving rise to the development of several efficient algorithms that cope with the computationally expensive task of association rule mining.In this study, different algorithms, Apriori, FP-tree and CHARM, for exploiting the hidden trends such as frequent itemsets, frequent patterns, closed frequent itemsets respectively, were discussed and their performances were evaluated. The perfomances of the algorithms were measured at different support levels, and the algorithms were tested on different data sets (on both synthetic and real data sets). The algorihms were compared according to their, data preparation performances, mining performance, run time performances and knowledge extraction capabilities.The Apriori algorithm is the most prevalent algorithm of association rule mining which makes multiple passes over the database aiming at finding the set of frequent itemsets for each level. The FP-Tree algorithm is a scalable algorithm which finds the crucial information as regards the complete set of prefix paths, conditional pattern bases and frequent patterns by using a compact FP-Tree based mining method. The CHARM is a novel algorithm which brings remarkable improvements over existing association rule mining algorithms by proving the fact that mining the set of closed frequent itemsets is adequate instead of mining the set of all frequent itemsets.Related to our experimental results, we conclude that the Apriori algorithm demonstrates a good performance on sparse data sets. The Fp-tree algorithm extracts less association in comparison to Apriori, however it is completelty a feasable solution that facilitates mining dense data sets at low support levels. On the other hand, the CHARM algorithm is an appropriate algorithm for mining closed frequent itemsets (a substantial portion of frequent itemsets) on both sparse and dense data sets even at low levels of support

Hybrid Association Rule Mining using AC Tree

In recent years, discovery of association rules among item sets in large database became popular. It gains its attention on research areas. Several association rule mining algorithms were developed for mining frequent item set. In this papers, a new hybrid algorithm for mining multilevel association rules called AC Tree i.e., AprioriCOFI tree was developed. This algorithm helps in mining association rules at multiple concept levels. The proposed algorithm works faster compared to traditional association rule mining algorithm and it is efficient in mining rules from large text documents. Keywords: Association rules, Apriori, FP tree, COFI tree, Concept hierarchy