Set Of Itemsets Research Articles

What to expect from a set of itemsets?

Dealing with redundancy is one of the main challenges in frequency based data mining and itemset mining in particular. To tackle this issue in the most objective possible way, we introduce the theoretical bases of a new probabilistic concept: Mutual constrained independence (MCI). Thanks to this notion, we describe a MCI model for the frequencies of all itemsets which is the least binding in terms of model hypotheses defined by the knowledge of the frequencies of some of the itemsets. We provide a method for computing MCI models based on algebraic geometry.We establish the link between MCI models and a class of MaxEnt models which has already known to be used in pattern mining. As such, our research presents further insight on the nature of such models and an entirely novel approach for computing them.

A guided FP-Growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data

Identifying frequent item-sets is a popular data-mining task. It consists of finding sets of items frequently appearing in data. Yet, finding all frequent item-sets in large or dense datasets may be time-consuming, and a user may be interested merely in some specific item-sets rather than all of them. Recently, methods have been proposed for targeted item-set mining; that is to calculate the support of some item-sets of interest. Though this approach is often more suitable for real applications than traditional item-set mining approaches, performance remains an issue. To address that issue, this paper presents a novel algorithm for multitude-targeted mining, named Guided Frequent Pattern-Growth (GFP-Growth). The GFP-Growth algorithm is designed to quickly mine a given set of item-sets using a small amount of memory. This paper proves that GFP-Growth yields the exact frequency-counts for each item-set of interest. It further shows that GFP-Growth can boost the performance for several problems requiring item-set mining. We specifically study the problem of generating minority-class rules from imbalanced data and develop the Minority-Report Algorithm (MRA) that uses GFP-Growth to solve this problem efficiently. We prove several theoretical properties of MRA and present experimental results showing substantial performance gain.

Regression Model Method for Analyze the Association Rules using Major Parameters

Using the data mining user can extract the information. Frequent itemsets is one of the popular task in data mining. Association Rule Analysis is the task of discovering association rules that occur frequently in a given large data set.The task is to find certain relationships among a set of itemsets in the database. There are two fundamental parameter(measurement) is Support and Confidence.Traditional association rule mining techniques employ predefined support and confidence values. But, it’s observed that specifying minimum support value of the minded rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system.This paper proposes a non-linear regression model using support, confidence and association rules. To predict the number of rules under the given explanatory variables say parameters. Use the R language for the Rules generations and also uses significance test to verify regression coefficients. Using the coefficient test and F-test verify the model

AFARTICA

Frequent item-set mining has been exhaustively studied in the last decade. Several successful approaches have been made to identify the maximal frequent item-sets from a set of typical item-sets. The present work has introduced a novel pruning mechanism which has proved itself to be significant time efficient. The novel technique is based on the Artificial Cell Division (ACD) algorithm which has been found to be highly successful in solving tasks that involve a multi-way search of the search space. The necessity conditions of the ACD process have been modified accordingly to tackle the pruning procedure. The proposed algorithm has been compared with the apriori algorithm implemented in WEKA. Accurate experimental evaluation has been conducted and the experimental results have proved the superiority of AFARTICA over apriori algorithm. The results have also indicated that the proposed algorithm can lead to better performance when the support threshold value is more for the same set of item-sets.

PENERAPAN DATA MINING UNTUK MENGOLAH TATA LETAK BUKU DENGAN METODE ASSOCIATION RULE

Data Mining is the development or discovery of new information by looking for certain patterns or rules from a large amount of data that is expected to overcome the existing problems data processing utilizes the data of borrowing transaction of book in the library ABC to be able to know which books are often borrowed, help to do preparation of book placement in accordance with the level of support and confidence so as to help the quality of service in the library better. This research uses association rule method and algorithm used by FP-Growth, microsoft excel database, and Rapidminer 5.3 to process data mining. The most important thing in data mining techniques is the rule for finding the highest frequency patterns between sets of itemsets. Data mining also has the goal to finding new knowledge frome processed data. Over last few decades, new methods have the ability to be developed around data collection and data generation. Data collection tools have provided us with large amounts of data.nthe data mining process has integrated techniques from various disciplines such as, statistics, learning technology database engine, pattern recognition, neural networks, information search and spatial analysis data. The mining engineering data has been used in such fields as bussines management, science, engineering, banking data management, administration, and many other applications.

Efficient hiding of confidential high-utility itemsets with minimal side effects

Privacy preserving data mining (PPDM) is an emerging research problem that has become critical in the last decades. PPDM consists of hiding sensitive information to ensure that it cannot be discovered by data mining algorithms. Several PPDM algorithms have been developed. Most of them are designed for hiding sensitive frequent itemsets or association rules. Hiding sensitive information in a database can have several side effects such as hiding other non-sensitive information and introducing redundant information. Finding the set of itemsets or transactions to be sanitised that minimises side effects is an NP-hard problem. In this paper, a genetic algorithm (GA) using transaction deletion is designed to hide sensitive high-utility itemsets for PPUM. A flexible fitness function with three adjustable weights is used to evaluate the goodness of each chromosome for hiding sensitive high-utility itemsets. To speed up the evolution process, the pre-large concept is adopted in the designed algorithm. It reduces the number of database scans required for verifying the goodness of an evaluated chromosome. Substantial experiments are conducted to compare the performance of the designed GA approach (with/without the pre-large concept), with a GA-based approach relying on transaction insertion and a non-evolutionary algorithm, in terms of execution time, side effects, database integrity and utility integrity. Results demonstrate that the proposed algorithm hides sensitive high-utility itemsets with fewer side effects than previous studies, while preserving high database and utility integrity.

Performance Comparison of Frequent Pattern Mining Algorithms for Business Intelligence Analytics

Objectives: In this paper, a simple and flexible partition algorithm has been proposed to mine frequent data item sets. This partition algorithm is different from other frequent pattern mining algorithm like Apriori algorithm, AprioriAllHybrid algorithm etc. Method: Partition algorithm concept has been proposed to increase the execution speed with minimum cost. Initially only for one time the database is scanned and separate partitions will be created for each sets of itemsets, which is 1-itemset, 2-itemsets, 3-itemsets etc. Findings: The scanning of whole database is not necessary to get the count of an itemset, it is enough to get the count of each data itemsets from its partition. This partition algorithm approach is implemented and evaluated against AprioriAllHybrid and Apriori algorithm. The candidate itemsets generated at each step is reduced and the scanning time is also reduced. The proposed methodology performance is significantly better than other algorithms and it promotes the faster execution time for mining frequent patterns. Applications: This proposed algorithm is used in areas like retail sales, production, universities, finance, banking systems and for business to plan and estimate the future values.

An Improved Eclat Algorithm for Mining Association Rules Based on Increased Search Strategy

Although Eclat algorithm is an efficient algorithm for mining association rules, there are some disadvantages which limit the efficient of Eclat. In this paper, we proposed an improved Eclat algorithm called Eclat_growth which is based on the increased search strategy. There are three main steps in the Eclat_growth algorithm. First, it scans the database and stores it into a table using vertical data format. Then, it builds an increased two-dimensional pattern tree and the TID_sets of itemsets in the vertical data format table are added into the pattern tree row by row. New frequent itemsets are generated by combining the new added item data with the existing frequent itemsets in the pattern tree. Finally, all frequent itemsets can be found by picking up all nodes of the pattern tree. In the process of generating new frequent itemsets, the prior knowledge is used to fully clip the candidate itemsets. In the process of generating an intersection of two itemsets and calculating the support degree, we proposed a new method called BSRI (Boolean array setting and retrieval by indexes of transactions) to reduce the run time. By comparing Eclat_growth with Eclat, Eclat-diffsets, Eclat-opt and hEclat, it is indicated that Eclat_growth has the highest performance in mining associating rules from various databases.

Structures of frequent itemsets and classifying structures of association rule set by order relations

This paper shows a mathematical foundation for almost important features in the problem of discovering knowledge by association rules. The sets of itemsets and association rules are partitioned into disjoint classes by two appropriate equivalence relations based on closures. The structure and unique representation of frequent itemsets are figured out through their generators and corresponding eliminable itemsets. Due to this structure, each equivalence rule class is split into different sets of basic and consequence rules according to an order relation. Indeed, the basic set comprises minimal elements basic rules whose forms are explicitly showed. Then, we propose operators to non-repeatedly deduce all consequence rules by adding, deleting or moving appropriate eliminable itemsets in both sides of basic rules. Further, we show that mining association rules based on a new order relation, min relation, is better than four other ones in terms of reductions in the time to extract basic rules, their cardinalities and rule lengths. These theoretical results are proven to be reliable. Experimental study on many benchmark databases shows the efficiency of the corresponding algorithms. Our approach e.g., the partitions of the frequent itemset and association rule sets is suitable to deal with big data because it can be exploited in parallel and distributed environment.

Itemset generalization with cardinality-based constraints

Generalized itemset mining is an established data mining technique that focuses on discovering high-level correlations among large databases. By exploiting a taxonomy built over the data items, items are aggregated into higher level concepts and, thus, data correlations at different abstraction levels can be discovered. However, since a large number of patterns can be extracted, the result of the mining process is often not easily manageable by domain experts.We propose a novel approach to discovering a compact subset of generalized itemsets from structured data. To guarantee model conciseness and readability, a set of itemsets that has a common generalization is generated only when its cardinality is so small that its manual inspection is practically feasible. Furthermore, generalizations are generated only when their knowledge is covered by a large number of low-level descendant itemsets, and the generalizations are worth considering in place of their many low-level descendants only in these cases.Experiments performed on synthetic, benchmark, and real data taken from a mobile application scenario demonstrate the effectiveness and efficiency of the proposed approach.

Cluster based bit vector mining algorithm for finding frequent itemsets in temporal databases

Abstract In this paper, we introduce an efficient algorithm using a new technique to find frequent itemsets from a huge set of itemsets called Cluster based Bit Vectors for Association Rule Mining (CBVAR). In this work, all the items in a transaction are converted into bits (0 or 1). A cluster is created by scanning the database only once. Then frequent 1-itemsets are extracted directly from the cluster table. Moreover, frequent k-itemsets, where k ≥ 2 are obtained by using Logical AND between the items in a cluster table. This approach reduces main memory requirement since it considers only a small cluster at a time and as scalable for any large size of database. The overall performance of this method is significantly better than that of the previously developed algorithms for effective decision making.

Mining maximal frequent itemsets from data streams

Frequent pattern mining from data streams is an active research topic in data mining. Existing research efforts often rely on a two-phase framework to discover frequent patterns: (1) using internal data structures to store meta-patterns obtained by scanning the stream data; and (2) re-mining the meta-patterns to finalize and output frequent patterns. The defectiveness of such a two-phase framework lies in the fact that the two stages provide barriers to dynamically and immediately finding frequent patterns with online functionalities. It is expected that a single-phase algorithm can fulfil frequent pattern mining from data streams in such a way that the users can see patterns in an immediate and dynamic manner, as soon as the patterns have become frequent. In this paper, we propose INSTANT, a single-phase algorithm for discovering frequent itemsets from data streams. The theoretical foundation of INSTANT is based on a framework theory on a set of itemsets, which is also presented in the paper. The novel design of INSTANT ensures that it employs compact data structures to mine frequent patterns from data streams in a single phase. Our experimental results demonstrate the time and space efficiency of the proposed algorithm.

An algorithm for discovering the frequent closed itemsets in a large database

Previous research revealed that the problem of discovering a complete set of frequent itemsets from a large database can be reduced to the problem of discovering the frequent closed itemsets, and this process results in a much smaller set of itemsets without information loss. This article is based on the observation that the set of all itemsets can be grouped into non-overlapping clusters such that each cluster is identified by a unique closed tidset. It is also found that there is only one closed itemset in each cluster and it is the superset of all itemsets with the same support. Therefore, the problem of discovering closed itemsets can be further considered as the problem of clustering the set of itemsets and then identifying each cluster by a unique closed tidset. This article presents CloseMiner, a new algorithm for discovering all frequent closed itemsets by grouping the set of itemsets into non-overlapping clusters. Experimental evaluation based on a number of real and synthetic databases has proved that CloseMiner outperforms the existing systems APRIORI and CHARM.