With the wide development of databases in general and data warehouses in particular, it is important to reduce the tasks that a database administrator must perform manually. The aim of auto-administrative systems is to administrate and adapt themselves automatically without loss (or even with a gain) in performance. The idea of using data mining techniques to extract useful knowledge for administration from the data themselves has existed for some years. However, little research has been achieved. This idea nevertheless remains a very promising approach, notably in the field of data warehousing, where queries are very heterogeneous and cannot be interpreted easily. The aim of this study is to search for a way of extracting useful knowledge from stored data themselves to automatically apply performance optimization techniques, and more particularly indexing techniques. We have designed a tool that extracts frequent itemsets from a given workload to compute an index configuration that helps optimizing data access time. The experiments we performed showed that the index configurations generated by our tool allowed performance gains of 15% to 25% on a test database and a test data warehouse.
Finding frequent itemsets in a data source is a fundamental operation behind Association Rule Mining. Generally, many algorithms use either the bottom-up or top-down approaches for finding these frequent itemsets. When the length of frequent itemsets to be found is large, the traditional algorithms find all the frequent itemsets from 1-length to n-length, which is a difficult process. This problem can be solved by mining only the Maximal Frequent Itemsets (MFS). Maximal Frequent Itemsets are frequent itemsets which have no proper frequent superset. Thus, the generation of only maximal frequent…
In order to generate synthetic basket data sets for better benchmark testing, it is important to integrate characteristics from real-life databases into the synthetic basket data sets. The characteristics that could be used for this purpose include the frequent itemsets and association rules. The problem of generating synthetic basket data sets from frequent itemsets is generally referred to as inverse frequent itemset mining. In this paper, we show that the problem of approximate inverse frequent itemset mining is {\bf NP}-complete. Then we propose and analyze an approximate algorithm for app…
The discovery of frequent itemsets can serve valuable economic and research purposes. Releasing discovered frequent itemsets, however, presents privacy challenges. In this paper, we study the problem of how to perform frequent itemset mining on transaction databases while satisfying differential privacy. We propose an approach, called PrivBasis, which leverages a novel notion called basis sets. A theta-basis set has the property that any itemset with frequency higher than theta is a subset of some basis. We introduce algorithms for privately constructing a basis set and then using it to find t…
In this paper we propose a R-step approximation to solve frequent itemset mining on quantum hardware like quantum annealing or QAOA. The idea is to search for the set of items where the minimal 2-item frequency is maximal. This can be represented as a maximum clique problem.
In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm etc., which take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA) we can improve the scenario. The major advantage of using GA in the discovery of frequent itemsets is that they perform global search and its time complexity is less compared to other algorithms as the genetic algorithm is based on the greedy approach. The main aim of this paper is to find all the frequent i…
In recent years, due to the wide applications of uncertain data, mining frequent itemsets over uncertain databases has attracted much attention. In uncertain databases, the support of an itemset is a random variable instead of a fixed occurrence counting of this itemset. Thus, unlike the corresponding problem in deterministic databases where the frequent itemset has a unique definition, the frequent itemset under uncertain environments has two different definitions so far. The first definition, referred as the expected support-based frequent itemset, employs the expectation of the support of a…
The Apriori algorithm is a classical algorithm for the frequent itemset mining problem. A significant bottleneck in Apriori is the number of I/O operation involved, and the number of candidates it generates. We investigate the role of LSH techniques to overcome these problems, without adding much computational overhead. We propose randomized variations of Apriori that are based on asymmetric LSH defined over Hamming distance and Jaccard similarity.
There are many algorithms developed for improvement the time of mining frequent itemsets (FI) or frequent closed itemsets (FCI). However, the algorithms which deal with the time of generating association rules were not put in deep research. In reality, in case of a database containing many FI/FCI (from ten thousands up to millions), the time of generating association rules is much larger than that of mining FI/FCI. Therefore, this paper presents an application of frequent closed itemsets lattice (FCIL) for mining minimal non-redundant association rules (MNAR) to reduce a lot of time for genera…
Frequent itemset mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied on standard (certain) transaction databases. Uncertain transaction databases consist of sets of existentially uncertain items. The uncertainty of items in transactions makes traditional techniques inapplicable. In this paper, we tackle the problem of finding probabilistic frequent itemsets based on possible world semantics. In this context, an itemset X is called frequent if the probability that X occurs in at least minSup transactions is above a given thresho…
Association rule has been an area of active research in the field of knowledge discovery. Data mining researchers had improved upon the quality of association rule mining for business development by incorporating influential factors like value (utility), quantity of items sold (weight) and more for the mining of association patterns. In this paper, we propose an efficient approach to find maximal frequent itemset first. Most of the algorithms in literature used to find minimal frequent item first, then with the help of minimal frequent itemsets derive the maximal frequent itemsets. These metho…
In this paper, we propose an algorithm of searching for both positive and negative itemsets of interest which should be given at the first stage for positive and negative association rules mining. Traditional association rule mining algorithms extract positive association rules based on frequent itemsets, for which the frequent itemsets, i.e. only positive itemsets of interest are searched. Further, there are useful itemsets among the frequent itemsets pruned from the traditional algorithms to reduce the search space, for mining of negative association rules. Therefore, the traditional algorit…
Data mining is the practice to search large amount of data to discover data patterns. Data mining uses mathematical algorithms to group the data and evaluate the future events. Association rule is a research area in the field of knowledge discovery. Many data mining researchers had improved upon the quality of association rule for business development by incorporating influential factors like utility, number of items sold and for the mining of association data patterns. In this paper, we propose an efficient algorithm to find maximal frequent itemset first. Most of the association rule algorit…
So far, most of association rule minings have considered about positive association rules based on frequent itemsets in databases[2,5-7], but they have not considered the problem of mining negative association rules correlated with frequent and infrequent itemsets. Negative association rule mining is much more difficult than positive association rule mining because it needs infrequent itemsets, and only the rare association rule mining which is a kind of negative association rule minings has been studied. This paper presents a mathematical model to mine positive and negative association rules …
The development of novel platforms and techniques for emerging "Big Data" applications requires the availability of real-life datasets for data-driven experiments, which are however out of reach for academic research in most cases as they are typically proprietary. A possible solution is to use synthesized datasets that reflect patterns of real ones in order to ensure high quality experimental findings. A first step in this direction is to use inverse mining techniques such as inverse frequent itemset mining (IFM) that consists of generating a transactional database satisfying given support co…
Mining frequent itemsets is an essential problem in data mining and plays an important role in many data mining applications. In recent years, some itemset representations based on node sets have been proposed, which have shown to be very efficient for mining frequent itemsets. In this paper, we propose DiffNodeset, a novel and more efficient itemset representation, for mining frequent itemsets. Based on the DiffNodeset structure, we present an efficient algorithm, named dFIN, to mining frequent itemsets. To achieve high efficiency, dFIN finds frequent itemsets using a set-enumeration tree wit…
Quantum Computing and Data Processing for Frequent Itemset Mining
National Taiwan University（台湾大学）；National Chengchi University（国立政治大学）；Academia Sinica（中央研究院）；The Pennsylvania State University（宾夕法尼亚州立大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）
Itemset mining has been an active area of research due to its successful application in various data mining scenarios including finding association rules. Though most of the past work has been on finding frequent itemsets, infrequent itemset mining has demonstrated its utility in web mining, bioinformatics and other fields. In this paper, we propose a new algorithm based on the pattern-growth paradigm to find minimally infrequent itemsets. A minimally infrequent itemset has no subset which is also infrequent. We also introduce the novel concept of residual trees. We further utilize the residua…
Frequent Pattern Mining is a one field of the most significant topics in data mining. In recent years, many algorithms have been proposed for mining frequent itemsets. A new algorithm has been presented for mining frequent itemsets based on N-list data structure called Prepost algorithm. The Prepost algorithm is enhanced by implementing compact PPC-tree with the general tree. Prepost algorithm can only find a frequent itemsets with required (pre-order and post-order) for each node. In this chapter, we improved prepost algorithm based on Hadoop platform (HPrepost), proposed using the Mapreduce …
Mining frequent itemsets from a transaction database has emerged as a fundamental problem in data mining and committed itself as a building block for many pattern mining tasks. In this paper, we present a general technique to reduce support checking time in existing depth-first search generate-and-test schemes such as Eclat/dEclat and PrePost+. Our technique allows infrequent candidate itemsets to be detected early. The technique is based on an early-stopping criterion and is general enough to be applicable in many frequent itemset mining algorithms. We have applied the technique to two TID-li…
Frequent itemset mining is a popular data mining technique. Apriori, Eclat, and FP-Growth are among the most common algorithms for frequent itemset mining. Considerable research has been performed to compare the relative performance between these three algorithms, by evaluating the scalability of each algorithm as the dataset size increases. While scalability as data size increases is important, previous papers have not examined the performance impact of similarly sized datasets that contain different itemset characteristics. This paper explores the effects that two dataset characteristics can…
In this paper we present a new error bound on sampling algorithms for frequent itemsets mining. We show that the new bound is asymptotically tighter than the state-of-art bounds, i.e., given the chosen samples, for small enough error probability, the new error bound is roughly half of the existing bounds. Based on the new bound, we give a new approximation algorithm, which is much simpler compared to the existing approximation algorithms, but can also guarantee the worst approximation error with precomputed sample size. We also give an algorithm which can approximate the top-$k$ frequent items…
Data aggregation in the setting of local differential privacy (LDP) guarantees strong privacy by providing plausible deniability of sensitive data. Existing works on this issue mostly focused on discovering heavy hitters, leaving the task of frequent itemset mining (FIM) as an open problem. To the best of our knowledge, the-state-of-the-art LDP solution to FIM is the SVSM protocol proposed recently. The SVSM protocol is mainly based on the padding and sampling based frequency oracle (PSFO) protocol, and regarded an itemset as an independent item without considering the frequency consistency am…
National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）
Mining frequent itemset using bit-vector representation approach is very efficient for dense type datasets, but highly inefficient for sparse datasets due to lack of any efficient bit-vector projection technique. In this paper we present a novel efficient bit-vector projection technique, for sparse and dense datasets. To check the efficiency of our bit-vector projection technique, we present a new frequent itemset mining algorithm Ramp (Real Algorithm for Mining Patterns) build upon our bit-vector projection technique. The performance of the Ramp is compared with the current best (all, maximal…
Simula Research Laboratory（西穆拉研究实验室）；LIRMM, University of Montpellier, CNRS（蒙彼利埃大学法国国家科学研究中心蒙彼利埃信息与机器人学实验室）
The problem of discovering frequent itemsets including rare ones has received a great deal of attention. The mining process needs to be flexible enough to extract frequent and rare regularities at once. On the other hand, it has recently been shown that constraint programming is a flexible way to tackle data mining tasks. In this paper, we propose a constraint programming approach for mining itemsets with multiple minimum supports. Our approach provides the user with the possibility to express any kind of constraints on the minimum item supports. An experimental analysis shows the practical ef…
Czech Technical University in Prague（布拉格捷克技术大学）；Institute of Computer Science（计算机科学研究所）
In the recent decade companies started collecting of large amount of data. Without a proper analyse, the data are usually useless. The field of analysing the data is called data mining. Unfortunately, the amount of data is quite large: the data do not fit into main memory and the processing time can become quite huge. Therefore, we need parallel data mining algorithms. One of the popular and important data mining algorithm is the algorithm for generation of so called frequent itemsets. The problem of mining of frequent itemsets can be explained on the following example: customers goes in a sto…
Apriori Algorithm is one of the most important algorithm which is used to extract frequent itemsets from large database and get the association rule for discovering the knowledge. It basically requires two important things: minimum support and minimum confidence. First, we check whether the items are greater than or equal to the minimum support and we find the frequent itemsets respectively. Secondly, the minimum confidence constraint is used to form association rules. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time and space for s…
As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is becoming a major challenge in data mining applications. In this work, we address significance in the context of frequent itemset mining. Specifically, we develop a novel methodology to identify a meaningful support threshold s* for a dataset, such that the number of itemsets with support at least s* represents a substantial deviation from what would be expected in a random dataset with the same number of transactions a…
Advances in data collection and data storage technologies have given way to the establishment of transactional databases among companies and organizations, as they allow enormous amounts of data to be stored efficiently. Useful knowledge can be mined from these data, which can be used in several ways depending on the nature of the data. Quite often companies and organizations are willing to share data for the sake of mutual benefit. However, the sharing of such data comes with risks, as problems with privacy may arise. Sensitive data, along with sensitive knowledge inferred from this data, mus…
The challenges with respect to mining frequent items over data streaming engaging variable window size and low memory space are addressed in this research paper. To check the varying point of context change in streaming transaction we have developed a window structure which will be in two levels and supports in fixing the window size instantly and controls the heterogeneities and assures homogeneities among transactions added to the window. To minimize the memory utilization, computational cost and improve the process scalability, this design will allow fixing the coverage or support at window…
In recent years, due to the wide applications of uncertain data (e.g., noisy data), uncertain frequent itemsets (UFI) mining over uncertain databases has attracted much attention, which differs from the corresponding deterministic problem from the generalized definition and resolutions. As the most costly task in association rule mining process, it has been shown that outsourcing this task to a service provider (e.g.,the third cloud party) brings several benefits to the data owner such as cost relief and a less commitment to storage and computational resources. However, the correctness integri…
FAST-National University of Computer and Emerging Sciences（FAST-国立计算机与新兴科学大学）
In this paper we present a novel hybrid (arraybased layout and vertical bitmap layout) database representation approach for mining complete Maximal Frequent Itemset (MFI) on sparse and large datasets. Our work is novel in terms of scalability, item search order and two horizontal and vertical projection techniques. We also present a maximal algorithm using this hybrid database representation approach. Different experimental results on real and sparse benchmark datasets show that our approach is better than previous state of art maximal algorithms.
FAST-National University of Computer and Emerging Science（FAST-国家计算机与新兴科学大学）
Real world datasets are sparse, dirty and contain hundreds of items. In such situations, discovering interesting rules (results) using traditional frequent itemset mining approach by specifying a user defined input support threshold is not appropriate. Since without any domain knowledge, setting support threshold small or large can output nothing or a large number of redundant uninteresting results. Recently a novel approach of mining only N-most/Top-K interesting frequent itemsets has been proposed, which discovers the top N interesting results without specifying any user defined support thre…
ALPINE is to our knowledge the first anytime algorithm to mine frequent itemsets and closed frequent itemsets. It guarantees that all itemsets with support exceeding the current checkpoint's support have been found before it proceeds further. Thus, it is very attractive for extremely long mining tasks with very high dimensional data (for example in genetics) because it can offer intermediate meaningful and complete results. This ANYTIME feature is the most important contribution of ALPINE, which is also fast but not necessarily the fastest algorithm around. Another critical advantage of ALPINE…
INESC-ID（葡萄牙系统与计算机工程研究所）；IST（高等技术学院）；Technical University of Lisbon（里斯本技术大学）
A new stream of research was born in the last decade with the goal of mining itemsets of interest using Constraint Programming (CP). This has promoted a natural way to combine complex constraints in a highly flexible manner. Although CP state-of-the-art solutions formulate the task using Boolean variables, the few attempts to adopt propositional Satisfiability (SAT) provided an unsatisfactory performance. This work deepens the study on when and how to use SAT for the frequent itemset mining (FIM) problem by defining different encodings with multiple task-driven enumeration options and search s…
Traditional association rule mining based on the support-confidence framework provides the objective measure of the rules that are of interest to users. However, it does not reflect the utility of the rules. To extract non-redundant association rules in support-confidence framework frequent closed itemsets and their generators play an important role. To extract non-redundant association rules among high utility itemsets, high utility closed itemsets (HUCI) and their generators should be extracted in order to apply traditional support-confidence framework. However, no efficient method exists at…
Efficient discovery of frequent itemsets in large datasets is a crucial task of data mining. In recent years, several approaches have been proposed for generating high utility patterns, they arise the problems of producing a large number of candidate itemsets for high utility itemsets and probably degrades mining performance in terms of speed and space. Recently proposed compact tree structure, viz., UP Tree, maintains the information of transactions and itemsets, facilitate the mining performance and avoid scanning original database repeatedly. In this paper, UP Tree (Utility Pattern Tree) is…
This paper presents a study of the characteristics of transactional databases used in frequent itemset mining. Such characterizations have typically been used to benchmark and understand the data mining algorithms working on these databases. The aim of our study is to give a picture of how diverse and representative these benchmarking databases are, both in general but also in the context of particular empirical studies found in the literature. Our proposed list of metrics contains many of the existing metrics found in the literature, as well as new ones. Our study shows that our list of metri…
We study the problem of frequent itemset mining in domains where data is not recorded in a conventional database but only exists in human knowledge. We provide examples of such scenarios, and present a crowdsourcing model for them. The model uses the crowd as an oracle to find out whether an itemset is frequent or not, and relies on a known taxonomy of the item domain to guide the search for frequent itemsets. In the spirit of data mining with oracles, we analyze the complexity of this problem in terms of (i) crowd complexity, that measures the number of crowd questions required to identify th…
BESTMOD Laboratory（BESTMOD实验室）；Institut Supérieur de Gestion（高等管理学院）
Mining association rules is a task of data mining, which extracts knowledge in the form of significant implication relation of useful items (objects) from a database. Mining multilevel association rules uses concept hierarchies, also called taxonomies and defined as relations of type 'is-a' between objects, to extract rules that items belong to different levels of abstraction. These rules are more useful, more refined and more interpretable by the user. Several algorithms have been proposed in the literature to discover the multilevel association rules. In this article, we are interested in th…
Frequent itemset mining has emerged as a fundamental problem in data mining and plays an important role in many data mining tasks, such as association analysis, classification, etc. In the framework of frequent itemset mining, the results are itemsets that are frequent in the whole database. However, in some applications, such recommendation systems and social networks, people are more interested in finding out the items that occur with some user-specified itemsets (query itemsets) most frequently in a database. In this paper, we address the problem by proposing a new mining task named top-k c…
Mining frequent itemsets through static Databases has been extensively studied and used and is always considered a highly challenging task. For this reason it is interesting to extend it to data streams field. In the streaming case, the frequent patterns' mining has much more information to track and much greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The output structure needs to be dynamically incremented to reflect the evolution of itemset frequencies over time. In this paper, we study this problem and specifically the methodology of…
The monotone duality problem is defined as follows: Given two monotone formulas f and g in iredundant DNF, decide whether f and g are dual. This problem is the same as duality testing for hypergraphs, that is, checking whether a hypergraph H consists of precisely all minimal transversals of a simple hypergraph G. By exploiting a recent problem-decomposition method by Boros and Makino (ICALP 2009), we show that duality testing for hypergraphs, and thus for monotone DNFs, is feasible in DSPACE[log^2 n], i.e., in quadratic logspace. As the monotone duality problem is equivalent to a number of pro…
Brown University（布朗大学）；University of Southern Denmark（南丹麦大学）
Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It requires to identify all itemsets appearing in at least a fraction $θ$ of a transactional dataset $\mathcal{D}$. Often though, the ultimate goal of mining $\mathcal{D}$ is not an analysis of the dataset \emph{per se}, but the understanding of the underlying process that generated it. Specifically, in many applications $\mathcal{D}$ is a collection of samples obtained from an unknown probability distribution $π$ on transactions, and by extracting the FIs in $\mathcal{D}$ one attempts to infer itemsets that are frequent…
It is generally well agreed that developing a unifying theory is one of the most important issues in Data Mining research. In the last two decades, a great deal of work has been devoted to the algorithmic aspects of the Frequent Itemset (FI) Mining problem. We are motivated by the need for formal modeling in the field. Thus, we introduce and analyze, in this theoretical study, a new model for the FI mining task. Indeed, we encode the itemsets as words over an ordered alphabet, and state this problem by a formal series over the counting semiring $(\mathbb{N},+,\times,0,1)$, whose range constitu…
Utility mining emerged to overcome the limitations of frequent itemset mining by considering the utility of an item. Utility of an item is based on user's interest or preference. Recently, temporal data mining has become a core technical data processing technique to deal with changing data. On-shelf utility mining considers on-shelf time period of item and gets the accurate utility values of itemsets in temporal database. In traditional on-shelf utility mining, profits of all items in databases are considered as positive values. However, in real applications, some items may have negative profi…
University of Bejaia（贝贾亚大学）；MT Atlantique（大西洋矿业电信学院）
Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint is perhaps the most significant-being the most well-studied and has the ability to encode any other constraints defined on finite variables. However, constraints can be very voluminous and their size can grow exponentially with their arity. To reduce space and the time complexity, researchers have focused on various forms of compression. In this paper we propose a new approach based on maximal frequent itemsets technique and area measure for e…
With the overwhelming amount of complex and heterogeneous data pouring from any-where, any-time, and any-device, there is undeniably an era of Big Data. The emergence of the Big Data as a disruptive technology for next generation of intelligent systems, has brought many issues of how to extract and make use of the knowledge obtained from the data within short times, limited budget and under high rates of data generation. Companies are recognizing that big data can be used to make more accurate predictions, and can be used to enhance the business with the help of appropriate association rule mi…
Data mining is a widely used technology for various real-life applications of data analytics and is important to discover valuable association rules in transaction databases. Interesting itemset mining plays an important role in many real-life applications, such as market, e-commerce, finance, and medical treatment. To date, various data mining algorithms based on frequent patterns have been widely studied, but there are a few algorithms that focus on mining infrequent or rare patterns. In some cases, infrequent or rare itemsets and rare association rules also play an important role in real-li…
University of Oran 1（奥兰第一大学）；University of Montpellier（蒙彼利埃大学）；University of Caen（卡昂大学）
Discovering the set of closed frequent patterns is one of the fundamental problems in Data Mining. Recent Constraint Programming (CP) approaches for declarative itemset mining have proven their usefulness and flexibility. But the wide use of reified constraints in current CP approaches raises many difficulties to cope with high dimensional datasets. This paper proposes CLOSED PATTERN global constraint which does not require any reified constraints nor any extra variables to encode efficiently the Closed Frequent Pattern Mining (CFPM) constraint. CLOSED-PATTERN captures the particular semantics…
Discovering frequent graph patterns in a graph database offers valuable information in a variety of applications. However, if the graph dataset contains sensitive data of individuals such as mobile phone-call graphs and web-click graphs, releasing discovered frequent patterns may present a threat to the privacy of individuals. {\em Differential privacy} has recently emerged as the {\em de facto} standard for private data analysis due to its provable privacy guarantee. In this paper we propose the first differentially private algorithm for mining frequent graph patterns. We first show that prev…
Here, we present a novel algorithm for frequent itemset mining for streaming data (FIM-SD). For the past decade, various FIM-SD methods in one-pass approximation settings have been developed to approximate the frequency of each itemset. These approaches can be categorized into two approximation types: parameter-constrained (PC) mining and resource-constrained (RC) mining. PC methods control the maximum error that can be included in the frequency based on a pre-defined parameter. In contrast, RC methods limit the maximum memory consumption based on resource constraints. However, the existing PC…
The tasks of extracting (top-$K$) Frequent Itemsets (FI's) and Association Rules (AR's) 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 FI's and AR's are sufficient for most practical uses, and a number of recent works explored the application of sampling for fast discovery of approximate solutions to the problems. However, these works do not provide satisfactory performance gua…
