Effective sanitization approaches to hide sensitive utility and frequent itemsets

Frequent itemset mining over dynamic data is an important problem in the context of knowledge discovery and data mining. Various data stream models are being used for mining frequent itemsets. In a data stream model the data arrive at high speed such that the algorithms used for mining data streams must process them in strict constraint of time and space. Due to emphasis over recent data and its bounded memory requirement, sliding window model is a widely used model for mining frequent itemset over data stream. In this paper we proposed an algorithm named Variable-Moment for mining both frequent and closed frequent itemset over data stream. The algorithm is appropriate for noticing latest or new changes in the set of frequent itemset by making its window size variable, which is determined dynamically based on the extent of concept drift occurring within the arriving data stream. The size of window expands when there is no concept drift in the arriving data stream and size shrinks when there is a concept change. The relative support instead of absolute support is being used for making the concept of variable window effective. The algorithm uses an in-memory data structure to store frequent itemsets. Data structure gets updated whenever a batch of transaction is added or deleted from the sliding window to output exact frequent itemsets. Extensive experiments on both real and synthetic data show that our algorithm excellently spots the concept changes and adapts itself to the new concept along data stream by adjusting window size.

Fast algorithms for hiding sensitive high-utility itemsets in privacy-preserving utility mining

The trends of data mining on healthcare data for improving medical services have increased because of the electronic healthcare record(EHR) system, which collects a massive amount of data on a daily basis. In the current scenario, hospital maintains its EHR system and stores the detailed information of patients. Data mining for healthcare improvement requires the data from all the EHR systems located at a different location to be stored at the central data mining server. Collection of healthcare data at some untrusted central data mining server raises privacy threats. Healthcare data contains patients’ private information and sharing this information for data mining creates privacy issues. Most of the previous research either focused on k-anonymity technique which causes information loss and decreases data mining accuracy or privacy preserving data mining which is focused on only specific data mining technique. We adopt source anonymous technique as privacy preserving scheme and present a novel scheme for healthcare data collection and mining in this paper. Our scheme collects data from all EHR systems without any information loss and stores at a single central data mining server, also ensuring privacy is preserved. Central data mining server helps to analyze the collected data with different data mining techniques (Association rule mining, Classification, Clustering, etc.) without the involvement of EHR systems. Our scheme is collusion resilient against central data mining server and EHR systems. Theoretical and experimental analysis show the efficiency of our scheme in terms of computation and communication cost. The experimental results using Heart disease dataset show the advantage to EHR systems using the proposed approach in terms of disease prediction accuracy.

Frequent itemset mining and association rule mining are fundamental problems in data mining. Despite of the intensive and continuous researches on frequent itemset mining, one essential and not completely solved drawback still remains. The drawback is pattern explosion: a huge number of frequent itemsets, or patterns, will be often derived. The association rule mining takes over the same drawback since each association rule consists of two frequent itemsets. One promising solution for alleviating this essential drawback practically as a post-processing is to rank the patterns and rules from various aspects and to identify which one to be examined first. In this paper, we propose to utilize the technology of representation learning to rank frequent itemsets and association rules. More specifically, we develop several evaluation measures for frequent itemsets and association rules by using the vector representations of items, transactions and frequent patterns which are obtained by existing representation learning techniques. By employing vector representations of three kinds of objects, various rankings reflecting different aspects can expect to be obtained.

In data mining finding out the frequent itemsets is one of the very essential topics. Data mining helps in identifying the best knowledge for different decision makers. Frequent itemset generation is the precondition and most time-consuming method for association rule mining. In this paper we suggest a new algorithm for frequent itemset detection that works with datasets in distributed manner. The proposed algorithm brings in a new method to find frequent itemset not including the necessitate to create candidate itemsets. The proposed approach could be implemented using horizontal representation for transaction datasets and allocating prime value. It explores all the frequent itemset that is present in the input and according to the support the maximum frequent itemset is identified. It was applied on different transactions database and compared with well-known algorithms: FP-Growth and Parallel Apriori with different support levels. The try out showed that the proposed algorithm attain major time improvement over both algorithms.

Data mining has made a significant impact on business and knowledge management in recent years. Data mining also known as KDD is a powerful new technology having great potential to analyze useful information stored in large data bases. Association rule mining is an active research area, used to find association and/or correlation among frequent item sets. Association rule mining discover frequent item sets with out considering total cost, quantity and number of items in a transaction. Utility based frequent item set mining is a new research area, which provide importance to sale quantity and price among items in a transaction. In this paper, we have proposed a novel approach for utility frequent item set mining. Our method mines novel frequent item sets by giving importance to items quantity, significance weightage, utility and user defined support. Our approach can be used to provide valuable recommendation to the enterprise to improve business utility.

Frequent itemsets mining is one of the interesting applications of data mining. Recently data mining has got a great deal of attention due to the explosive growth in data and the economic and scientific need for turning such data into useful information. However, the traditional frequent itemsets mining algorithms have become inefficient to work with large datasets effectively on a single machine due to computational power and memory limits. Current methods prefer to control the execution time and output by using higher minimum support thresholds, which lead to less candidates and frequent itemsets. In this paper, an improved-version of Apriori like HFDM-EB algorithm that can deal with lower minimum support thresholds is proposed for mining frequent itemsets over big transactional data on Hadoop framework and by utilizing compressed bitmaps. The experimental results show that the improved algorithm is efficient and scalable for mining frequent itemsets in big data.

With the rapid development of big data and data analysis technology, a large amount of data is collected and used for data mining. However, the prediction accuracy of data mining often fails to meet the needs of third-party data users, and holders may not explicitly and openly share their data. How to share data and handle data privacy issues between entities while ensuring error-free prediction becomes a challenging problem. Therefore, privacy preserving data mining(PPDM) technology was introduced. Among them, the random perturbation method is a direct and effective method to protect data privacy by modifying some sensitive attribute values. This paper proposes a new method of data mining for privacy protection, which is based on SVM machine learning algorithm for data mining and great balances the utility and security of data. JHI and WBC data sets in machine learning database were used in the experiment, and machine learning privacy protection parameters were used for security evaluation. Experiments were conducted to compare NMF and NMFSVD algorithms, and the experiments results show that RNP privacy protection data mining method not only has less error, but also can better protect the privacy of the data set.

Mining frequent itemsets is a fundamental task in data mining. Unfortunately the number of frequent itemsets describing the data is often too large to comprehend. This problem has been attacked by condensed representations of frequent itemsets that are subcollections of frequent itemsets containing only the frequent itemsets that cannot be deduced from other frequent itemsets in the subcollection, using some deduction rules. In this paper we review the most popular condensed representations of frequent itemsets, study their relationship to transaction databases and each other, examine their combinatorial and computational complexity, and describe their relationship to other important concepts in combinatorial data analysis, such as Vapnik-Chervonenkis dimension and hypergraph transversals.

Pattern mining is a fundamental problem in data mining. The problem is to find all the patterns appearing in the given database frequently. For a set E D f1; : : : ; ng of items, an itemset (also called a pattern) is a subset of E. Let D be a given database composed of transactions R1; : : : ; Rm, Ri E. For an itemset P , an occurrence of P is a transaction of D such that P R, and the occurrence set Occ.P / is the set of occurrences of P . The frequency of P , also called support, is jOcc.P /j and denoted by frq.P /. For a given constant called minimum support, an itemset P is frequent if frq.P / . For given a database and a minimum support, frequent itemset mining is the problem of enumerating all frequent itemsets in D.

This article describes a watchdog program that discovers “meaningful” repair cases from a field service database. “Meaningful” cases are those judged worth probing further to prevent an epidemic of quality problems. Our system has employed the apriori algorithm, a data mining technique that efficiently performs the basket analysis. Our system proves that this data mining technique is not only useful in knowledge discovery but is also capable of performing the database watchdog task. The apriori algorithm automatically generates frequent itemsets from a large set of records. A frequent itemset is an arbitrary combination of values that appear more often than a threshold “minimum support.” The algorithm often generates too many itemsets for quality engineers to review carefully in their daily work. Many itemsets do not provide sufficient information to investigate further. Hence, in order not to generate these valueless itemsets, the apriori algorithm is modified in two ways. One way is “basket analysis on objective and explanatory attributes” and the other is “itemset reduction.” The advantage of our method is demonstrated with some experimental results. © 2002 Wiley Periodicals, Inc. Electr Eng Jpn, 140(2): 18–25, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10034

Privacy preserving rule-based classifier using modified artificial bee colony algorithm

The tasks of extracting (top-K) Frequent Itemsets (FIs) and Association Rules (ARs) are fundamental primitives in data mining and database applications. Exact algorithms for these problems exist and are widely used, but their running time is hindered by the need of scanning the entire dataset, possibly multiple times. High-quality approximations of FIs and ARs are sufficient for most practical uses. Sampling techniques can be used for fast discovery of approximate solutions, but works exploring this technique did not provide satisfactory performance guarantees on the quality of the approximation due to the difficulty of bounding the probability of under- or oversampling any one of an unknown number of frequent itemsets. We circumvent this issue by applying the statistical concept ofVapnik-Chervonenkis (VC) dimensionto develop a novel technique for providing tight bounds on the sample size that guarantees approximation of the (top-K) FIs and ARs within user-specified parameters. The resulting sample size is linearly dependent on the VC-dimension of a range space associated with the dataset. We analyze the VC-dimension of this range space and show that it is upper bounded by an easy-to-compute characteristic quantity of the dataset, thed-index, namely, the maximum integerdsuch that the dataset contains at leastdtransactions of length at leastdsuch that no one of them is a superset of or equal to another. We show that this bound is tight for a large class of datasets. The resulting sample size is a significant improvement over previous known results. We present an extensive experimental evaluation of our technique on real and artificial datasets, demonstrating the practicality of our methods, and showing that they achieve even higher quality approximations than what is guaranteed by the analysis.

Vertical itemset mining is an important frequent pattern mining problem with broad applications. It is challenging since one may need to examine a combinatorial explosive number of possible patterns of items of a dataset in a traditional horizontal algorithm. Since high dimensional datasets typically contain a large number of columns and a small number of rows, vertical itemset mining algorithms, which extract the frequent itemsets of dataset by producing all combination of rows ids, are a good alternative for horizontal algorithms in mining frequent itemsets from high dimensional dataset. Since a rowset can be simply produced from its subsets by adding a new row id to a sub rowset, many bottom up vertical itemset mining algorithms are designed and represented in the literature. However, bottom up vertical mining algorithms suffer from a main drawback. Bottom-up algorithms start the process of generating and testing of rowsets from the small rowsets and go on to the larger rowsets, whereas the small rowsets cannot produce a frequent itemsets because they contain less than minimum support threshold number of rows. In this paper, we described a new efficient vertical top down algorithm called VTD (Vertical Top Down) to conduct mining of frequent itemsets in high dimensional datasets. Our top down approach employed the minimum support threshold to prune the rowsets which any itemset could not be extracted from them. Several experiments on real bioinformatics datasets showed that VTD is orders of magnitude better than previous closed pattern mining algorithms. Our performance study showed that this algorithm outperformed substantially the best former algorithms.

Discovering frequent itemsets is a key problem in important data mining applications, such as the discovery of association rules, strong rules, episodes, and minimal keys. Typical algorithms for solving this problem operate in a bottom-up breadth-first search direction. The computation starts from frequent 1-itemsets (minimal length frequent itemsets) and continues until all maximal (length) frequent itemsets are found. During the execution, every frequent itemset is explicitly considered. Such algorithms perform reasonably well when all maximal frequent itemsets are short. However, performance drastically decreases when some of the maximal frequent itemsets are relatively long. We present a new algorithm which combines both the bottom-up and top-down directions. The main search direction is still bottom-up but a restricted search is conducted in the top-down direction. This search is used only for maintaining and updating a new data structure we designed, the maximum frequent candidate set. It is used to prune candidates in the bottom-up search. As a very important characteristic of the algorithm, it is not necessary to explicitly examine every frequent itemset. Therefore it performs well even when some maximal frequent itemsets are long. As its output, the algorithm produces the maximum frequent set, i.e., the set containing all maximal frequent itemsets, which therefore specifies immediately all frequent itemsets. We evaluate the performance of the algorithm using a well-known benchmark database. The improvements can be up to several orders of magnitude, compared to the best current algorithms.