Minimally Infrequent Itemset Mining Using Pattern-Growth Paradigm

IJSRD, 2013

The analysis of observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner. To find the association rules among the transactional dataset is the main problem of frequent itemset mining. Many techniques have been developed to increase the efficiency of mining frequent itemsets. In this paper, we denote a new method for generating frequent itemsets using frequent itemset tree (FI-tree). Also we describe the example of new method and its result analysis using wine dataset. Our method execution time is better compare to SaM method.

2012

A new approach to mine all frequent itemsets from a transaction database is proposed. The main features of this paper are as follows: (1) the proposed algorithm performs database scanning only once to construct a data structure called an inverted index structure (IIS); (2) the change in the minimum support threshold is not affected by this structure, and as a result, a rescan of the database is not required; and (3) the proposed mining algorithm, IIS-Mine, uses an efficient property of an extendable itemset, which reduces the recursiveness of mining steps without generating candidate itemsets, allowing frequent itemsets to be found quickly. We have provided definitions, examples, and a theorem, the completeness and correctness of which is shown by mathematical proof. We present experiments in which the run time, memory consumption and scalability are tested in comparison with a frequent-pattern (FP) growth algorithm when the minimum support threshold is varied. Both algorithms are evaluated by applying them to synthetics and real-world datasets. The experimental results demonstrate that IIS-Mine provides better performance than FP-growth in terms of run time and space consumption and is effective when used on dense datasets.

2011

An important problem of interactive data mining is "to find frequent item sets contained in a subset C of set of all items on a given database". Reducing the database on C or incorporating it into an algorithm for mining frequent item sets (such as Charm-L, Eclat) and resolving the problem are very time consuming, especially when C is often changed. In this paper, we propose an efficient approach for mining them as follows. Firstly, it is necessary to mine only one time from database the class LGA containing the closed item sets together their generators. After that, when C is changed, the class of all frequent closed item sets and their generators on C is determined quickly from LGA by our algorithm MINE_CG_CONS. We obtain the algorithm MINE_FS_CONS to mine and classify efficiently all frequent item sets with constraint from that class. Theoretical results and the experiments proved the efficiency of our approach.

Frequent itemset mining is an exploratory data mining technique widely used for discovering valuable correlations among data. Frequent itemset mining is a core component of data mining and variations of association analysis, like association rule mining. Extraction of frequent itemsets is a core step in many association analysis techniques. The frequent occurrence of item is expressed in terms of the support count. An item is said to be frequent whose number of occurrences is greater than threshold value. Recently, Infrequent Item sets are also considered to be important in various situations. Infrequent Item set mining is the just the variation of frequent item set mining, where it takes the rarely occurred items in the database. The use of infrequent item set mining is to help for the decision making systems. There are several existing algorithms like Apriori, F-Miner, FP-growth, Residual tree algorithm, Fast algorithm to mine the frequent item sets which takes more computational time. The proposed system is to mine the infrequent item sets by mathematical modeling technique(NFA)where, results in less computing time.

2007

Discovery of frequent itemsets is an important problem in Data Mining. Most of the previous research based on Apriori, which suffers with generation of huge number of candidate itemsets and performs repeated passes for finding frequent itemsets. To address this problem, we propose an algorithm for finding frequent K-itemsets in which the itemsets whose length is less than K will be pruned from the database and will not be considered for further processing which reduces the size and number of comparisons to be performed. In addition to this, it generates 1-itemset as a data pre processing step which saves time and makes execution fast. The experimental results are included.

As with the advancement of the information technologies, the amount of accumulated data is also increasing. It has resulted in large amount of data stored in databases, warehouses and other repositories. Thus the Data mining comes into picture to explore and analyse the databases to extract the interesting and previously unknown patterns and rules known as association rule mining. In data mining, association rule mining becomes one of the important tasks of descriptive technique which can be defined as discovering meaningful patterns from large collection of data. Mining frequent itemset is very fundamental part of association rule mining. Many algorithms have been proposed from last many decades including horizontal layout based techniques, vertical layout based techniques, and projected layout based techniques. But most of the techniques suffer from repeated database scan, Candidate generation (Apriori Algorithms), memory consumption problem (FP-tree Algorithms) and many more for mining frequent patterns. As in retailer industry many transactional databases contain same set of transactions many times, to apply this thought, in this paper we present a new technique which is combination of present Apriori (improved Apriori) and FP-tree techniques that guarantee the better performance in terms of time and memory than classical aprioi algorithm.

Most of studies for mining frequent patterns are based on constructing tree for arranging the items to mine frequent patterns. Many algorithms proposed recently have been motivated by FP-Growth (Frequent Pattern Growth) process and uses an FP-Tree (Frequent Pattern Tree) to mine frequent patterns. In this paper we propose algorithm called FP-Growth-Graph and CATS Tree which uses graph and tree data structure to arrange the items for mining frequent item sets. CATS Tree extends the idea of FP Tree to improve storage compression and allow frequent pattern mining without generation of candidate itemsets.FP-Growth-Graph contain three main part, first is to scan the database only once ,the second is to prune non-frequent item and then construct FP-Graph.

2008 5th International Conference on Electrical Engineering, Computing Science and Automatic Control, 2008

In this paper, we propose a new algorithm for mining frequent itemsets. This algorithm is named AMFI (Algorithm for Mining Frequent Itemsets). This algorithm compresses the data while maintaining the necessary semantics for the frequent itemsets mining problem and it is more efficient that traditional compression algorithms. The AMFI efficiency is based on a compressed vertical binary representation of the data and on a very fast support count. AMFI performs a breadth first search through equivalence classes. We compare our proposal with an implementation using PackBits algorithm.

… Conference on Data …, 2008

Frequent itemsets mining plays an important role in data mining research for over a decade. However, the mining of the all frequent itemsets will lead to a massive number of itemsets. Fortunately, this problem can be reduced to the mining of maximal frequent itemsets. In this paper, we propose a new method for mining maximal frequent itemsets. Our method introduces an efficient database encoding technique, a novel tree structure called PC_Tree and also PC_Miner algorithm. The database encoding technique utilizes Prime number characteristics and transforms each transaction into a positive integer that has all properties of its items. The PC_Tree is a simple tree structure but yet powerful to capture whole of transactions by one database scan. The PC_Miner algorithm traverses the PC_Tree to mine maximal frequent itemsets. Experiments verify the efficiency and advantages of the proposed method.

Mining patterns that occur in a certain time frame is a well defined data mining problem. Many researchers have proposed different methods for the same. Taking time factor into consideration, different patterns can be extracted. These patterns cannot be extracted normally. These patterns are known as temporal patterns. Here we propose an efficient method of mining locally frequent patterns from temporal datasets. The real time databases are used to prove the efficacy of the method. The comparative studies prove that our method gives more results than the other well known method.