Number Of Itemsets Research Articles

Using Clustering Analysis and Association Rule Technology in Cross-Marketing

In this paper, according to the perspective of customers and products, by using clustering analysis and association rule technology, this paper proposes a cross-marketing model based on an improved sequential pattern mining algorithm, where an improved algorithm AP (Apriori all PrefixSpan) is applied. The algorithm can reduce the time cost of constructing a projection database and the influence of the increase of support on the algorithm efficiency. The improved idea is that when the first partition is used to generate the projection database, the number of itemsets in the projection database is sorted from small to large, and when the second partition is used, the sequence patterns are generated directly from the mined sequence patterns, so as to reduce the construction of the database. The experimental results show that this method can quickly mine the effective information in complex data sets, improve the accuracy and efficiency of data mining, and occupy less memory consumption, which has good theoretical value and application value.

IMPLEMENTASI ALGORITMA APRIORI UNTUK PREDIKSI STOK PERALATAN TULIS PADA TOKO XYZ

Bagi kalangan anak sekolah, perkantoran, maupun kalangan mahasiswa kebutuhan peralatan tulis diperlukan. Dikarenakan kebutuhan peralatan tulis diperlukan untuk mecatat hal - hal penting. Walaupun di era digitalisasi sekarang ini materi ? materi pembelajaran bisa didapatkan secara online untuk meminimalisir penggunaan peralatan tulis, namun tidak menutup kemungkinan bahwa kebutuhan peralatan tulis memang diperlukan untuk kegiatan ? kegiatan lain. Oleh sebab itu, toko yang menyediakan berbagai macam peralatan tulis harus bisa mengetahui tingkat pembelian pada produk ? produk yang lebih sering dibeli oleh konsumen agar penyediaan stok produk tersebut bisa lebih terfokus pada produk ? produk yang lebih sering dibeli oleh konsumen. Pada penulisan ini, penulis menggunakan algoritma Apriori yang melakukan pencarian frekuensi itemset untuk mendapatkan nilai aturan asosiasi yang memenuhi syarat minimum support dan syarat minimum confidence dengan beberapa jumlah itemset dalam keseluruhan transaksinya untuk mencapai nilai persentasi support dan nilai persentasi confident terbaik dalam menentukan item yang sering terjual dalam setiap transaksi. Dengan menggunakan algoritma Apriori yang memiliki tingkat akurasi sekitar 70%, dapat memprediksi stok penjualan produk pada toko XYZ. Tujuan dari penerapan algoritma Apriori pada penulisan ini adalah untuk mengetahui tingkat pembelian pada produk ? produk yang lebih sering dibeli oleh konsumen berdasarkan data transaksi yang ada untuk penyediaan stok produk penjualan yang lebih efisien.

Efficient Algorithm for Mining High Utility Pattern Considering Length Constraints

High utility itemset (HUI) mining is one of the popular and important data mining tasks. Several studies have been carried out on this topic, which often discovers a very large number of itemsets and rules, which reduces not only the efficiency but also the effectiveness of HUI mining. In order to increase the efficiency and discover more interesting HUIs, constraint-based mining plays an important role. To address this issue, the authors propose an algorithm to discover HUIs with length constraints named EHIL (Efficient High utility Itemsets with Length constraints) to decrease the number of HUIs by removing tiny itemsets. EHIL adopts two new upper bound named sub-tree and local utility for pruning and modify them by incorporating length constraints. To reduce the dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. The execution time improvements ranged from a modest five percent to two orders of magnitude across benchmark datasets. The memory usage is up to twenty-eight times less than state-of-the-art algorithm FHM+.

Параллельный поиск частых наборов на многоядерных ускорителях Intel MIC

Association rule mining is one of the basic problems of data mining, which supposes finding strong correlations between itemsets in large transaction database. Association rules are generated from frequent itemsets (itemset is frequent if its items frequent occur together in transactions). The DIC (Dynamic Itemset Counting) algorithm is modification of the classical Apriori algorithm of finding frequent itemsets. DIC tries to reduce the number of passes made over the transaction database while keeping the number of itemsets counted in a pass relatively low. The paper addresses the task of accelerating DIC on the Intel MIC (Many Integrated Core) systems in the case when the transaction database fits into the main memory. The paper presents a parallel implementation of DIC based on OpenMP technology and thread-level parallelism. We exploit the bit-based internal layout for transactions and itemsets. This technique simplifies the support count via logical bitwise operation, and allows for vectorization of such a step. Experiments with large synthetic and real databases showed good performance and scalability of the proposed algorithm.

Towards Scalable Algorithm for Closed Itemset Mining in High-Dimensional Data

<p>Mining frequent itemsets from large dataset has a major drawback in which the explosive number of itemsets requires additional mining process which might filter the interesting ones. Therefore, as the solution, the concept of closed frequent itemset was introduced that is lossless and condensed representation of all the frequent itemsets and their corresponding supports. Unfortunately, many algorithms are not memory-efficient since it requires the storage of closed itemsets in main memory for duplication checks. This paper presents BFF, a scalable algorithm for discovering closed frequent itemsets from high-dimensional data. Unlike many well-known algorithms, BFF traverses the search tree in breadth-first manner resulted to a minimum use of memory and less running time. The tests conducted on a number of microarray datasets show that the performance of this algorithm improved significantly as the support threshold decreases which is crucial in generating more interesting rules.</p>

Multiobjective Adaptive Fuzzy C-Means Algorithm for Mining Frequent Closed Itemset: A Lattice Based Approach for Time Series Data

Of late, frequent itemset mining from the data bases has emerged as a highly challenging task. And, there is a feast of various approaches launched with an eye on extracting the frequent rules from the database. In the erstwhile frequent itemset mining technique, the frequent itemset are extracted from the time series data by means of Adaptive Fuzzy C-Means (AFCM) clustering, though it is saddled with several setbacks in the clustering procedure. This arises on account of the fact that in clustering, the number of itemset is mammoth, and hence the method consumes a lot of time in addition to spending enhanced scanning time. Therefore, with a view tackling these adverse features, an innovative frequent itemset mining method is suggested in this document. At the outset, the time sequence database values are grouped by means of the Multiobjective AFCM method. Subsequently, the frequent itemset are extracted from the grouped outcomes by employing the lattice based method. The novel technique is performed in the MATLAB platform and its excellence is appraised by means of rainfall database.

Efficient Mining of Profit Rules from Closed Inter-Transaction Itemsets

Data mining applications in financial sectors are very common since investors can apply the resultant rules to make profits. Profit mining algorithms in particular, such as PRMiner, can generate profit rules that meet the expectations of investors regarding profit, risk, and win rate. However, most of such algorithms are not efficient due to the long processing time involving going through the whole search space in complex dynamical systems of financial markets. Hence, we propose a new approach in this paper to solve the problem by using closed itemsets to obtain profit rules without processing the entire trading rules. Based on the inter-day modeling, we analyze inter-transactions and conduct trading simulations to predict trading results for efficient profit rule generation. We develop two algorithms of JCMiner and ATMiner to process closed itemsets, which have better performance than the approach of PRMiner, especially for the large number of itemsets and large datasets. According to the experimental results, our algorithms outperform PRMiner in various experimental scenarios, i.e., mining parameters, the number of items in a transaction, and the number of transactions in a dataset.

CP-tree: An adaptive synopsis structure for compressing frequent itemsets over online data streams

Due to the characteristics of a data stream, it is very important to confine the memory usage of a data mining process. This paper proposes a CP-tree (Compressible-prefix tree) that can be effectively employed in finding frequent itemsets over an online data stream. Unlike a prefix tree, a node of a CP-tree can maintain a concise synopsis that can be used to trace the supports of several itemsets together. As the number of itemsets that are traced by a node of a CP-tree is increased, the size of a CP-tree becomes smaller. However, the result of a CP-tree becomes less accurate since the estimated supports of those itemsets that are traced together by a node of a CP-tree may contain possible false positive or negative errors. Based on this characteristic, the size of a CP-tree can be controlled by merging or splitting the nodes of a CP-tree, which allows the utilization of a confined memory space as much as possible. Therefore, the accuracy of a CP-tree is maximized at all times for a confined memory space. Furthermore, a CP-tree can trace a concise set of representative frequent itemsets that can collectively represent the set of original frequent itemsets.

The Research of Improved Apriori Algorithm Application in Distance Education Platform

This paper points out the bottleneck of classical Apriori algorithm, presents an improved association rule mining algorithm based on Apriori algorithm.The new algorithm is based on pruing away the itemsets whose support degree is less than minsupport to reduce the number of itemsets in the transaction database. At the same time the new algorithm change the candidate_gen function to generate a continuous access page. According to the running result of the algorithm, the processing time of mining is decreased and the efficiency of algorithm has improved.Whats more, the new algorithm can find the learners frequent traversal path to improve the intelligence of the distance education platform. Keywords: Associaion Rules;Apriori Algorithm; Frequent Traversal Path;Distance Education Platform

A Survey of Genetic Algorithm for Association Rule Mining

recent years, Data Mining is an important aspect for generating association rules among the large number of itemsets. Association Rule Mining is the method for discovering interesting relations between variables in large databases. It is considered as one of the important tasks of data mining intended towards decision making. Genetic algorithm (GA) based on evolution principles has found its strong base in mining Association Rules. Genetic algorithm is a search heuristic which is used to generate useful solutions to optimization and search problems. Genetic algorithm has proved to generate more accurate results when compared to other formal methods available. The fitness function used in Genetic Algorithm evaluates the quality of each rule. Many researchers have proposed genetic algorithm for mining interesting rules from dataset. This paper presents the survey of Genetic Algorithm for Association Rule Mining.

An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets

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 and the same individual item frequencies. These itemsets can then be flagged as statistically significant with a small false discovery rate. We present extensive experimental results to substantiate the effectiveness of our methodology.

SAS: Implementation of scaled association rules on spatial multidimensional quantitative dataset

Mining spatial association rules is one of the most important branches in the field of Spatial Data Mining (SDM). Because of the complexity of spatial data, a traditional method in extracting spatial association rules is to transform spatial database into general transaction database. The Apriori algorithm is one of the most commonly used methods in mining association rules at present. But a shortcoming of the algorithm is that its performance on the large database is inefficient. The present paper proposed a new algorithm by extracting maximum frequent itemsets based on spatial multidimensional quantitative dataset. Algorithms for mining spatial association rules are similar to association rule mining except consideration of special data, the predicates generation and rule generation processes are based on Apriori. The proposed method (SAS) Scaled Aprori on Spatial multidimensional quantitative dataset in the paper reduces the number of itemsets generated and also improves the execution time of the algorithm.

Data Stream Frequent Closed Item Sets Mining Based on Fast Sliding Window

According to the mobility and continuity of the flow of data streams，this paper presents an algorithm called NSWR to mine the frequent item sets from a fast sliding window over data streams and it meets people’s needs of getting the frequent item sets over data that recently arrive. NWSR, using an effective bit-sequence representation of items based on the data stream sliding window, helps to store data; to support different support threshold value inquiry through hash-table-based frequent closed item sets results query method; to offer screening method based on the classification of closed item sets for reducing the number of item sets that need closure judgments, effectively reducing the computational complexity. Experiments show that the algorithm has better time and space efficiency.

Maximal Frequent Itemsets in Data Stream Mining Based on Orderly-Compound Policy

Mining maximal frequent itemsets get the advantage of a relatively small number of itemsets. Compared to mining frequent itemsets and mining frequent closed itemsets, such algorithm has higher time and space efficiency. According to the features of data streams and combined sliding window, a new algorithm E-FPMFI which is based on orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. The algorithm based on basic window updates information from data stream flow fragment and scans the stream only once to gain and store it in frequent itemsets list. The algorithm construct FP-tree, then compress orderly FP-tree by merging nodes which has equal minsup in same branch, also uses subset mix pruning technique, avoid superset checking. The experimental results show the algorithm has higher time, space efficiency and good scalability.

Summary queries for frequent itemsets mining

There are many advanced techniques that can efficiently mine frequent itemsets using a minimum-support. However, the question that remains unanswered is whether the minimum-support can really help decision makers to make decisions. In this paper, we study four summary queries for frequent itemsets mining, namely, (1) finding a support-average of itemsets, (2) finding a support-quantile of itemsets, (3) finding the number of itemsets that greater/less than the support-average, i.e., an approximated distribution of itemsets, and (4) finding the relative frequency of an itemset (compared its frequency with that of other itemsets in the same dataset). With these queries, a decision maker will know whether an itemset in question is greater/less than the support-quantile; the distribution of itemsets; and the frequentness of an itemset. Processing these summary queries is challenging, because the minimum-support constraint cannot be used to prune infrequent itemsets. In this paper, we propose several simple yet effective approximation solutions. We conduct extensive experiments for evaluating our strategy, and illustrate that the proposed approaches can well model and capture the statistical parameters (summary queries) of itemsets in a database.

A new concise representation of frequent itemsets using generators and a positive border

A complete set of frequent itemsets can get undesirably large due to redundancy when the minimum support threshold is low or when the database is dense. Several concise representations have been previously proposed to eliminate the redundancy. Generator based representations rely on a negative border to make the representation lossless. However, the number of itemsets on a negative border sometimes even exceeds the total number of frequent itemsets. In this paper, we propose to use a positive border together with frequent generators to form a lossless representation. A positive border is usually orders of magnitude smaller than its corresponding negative border. A set of frequent generators plus its positive border is always no larger than the corresponding complete set of frequent itemsets, thus it is a true concise representation. The generalized form of this representation is also proposed. We develop an efficient algorithm, called GrGrowth, to mine generators and positive borders as well as their generalizations. The GrGrowth algorithm uses the depth-first-search strategy to explore the search space, which is much more efficient than the breadth-first-search strategy adopted by most of the existing generator mining algorithms. Our experiment results show that the GrGrowth algorithm is significantly faster than level-wise algorithms for mining generator based representations, and is comparable to the state-of-the-art algorithms for mining frequent closed itemsets.

A false negative approach to mining frequent itemsets from high speed transactional data streams

Mining frequent itemsets from transactional data streams is challenging due to the nature of the exponential explosion of itemsets and the limit memory space required for mining frequent itemsets. Given a domain of I unique items, the possible number of itemsets can be up to 2 I − 1. When the length of data streams approaches to a very large number N, the possibility of an itemset to be frequent becomes larger and difficult to track with limited memory. The existing studies on finding frequent items from high speed data streams are false-positive oriented. That is, they control memory consumption in the counting processes by an error parameter ϵ, and allow items with support below the specified minimum support s but above s − ϵ counted as frequent ones. However, such false-positive oriented approaches cannot be effectively applied to frequent itemsets mining for two reasons. First, false-positive items found increase the number of false-positive frequent itemsets exponentially. Second, minimization of the number of false-positive items found, by using a small ϵ, will make memory consumption large. Therefore, such approaches may make the problem computationally intractable with bounded memory consumption. In this paper, we developed algorithms that can effectively mine frequent item(set)s from high speed transactional data streams with a bound of memory consumption. Our algorithms are based on Chernoff bound in which we use a running error parameter to prune item(set)s and use a reliability parameter to control memory. While our algorithms are false-negative oriented, that is, certain frequent itemsets may not appear in the results, the number of false-negative itemsets can be controlled by a predefined parameter so that desired recall rate of frequent itemsets can be guaranteed. Our extensive experimental studies show that the proposed algorithms have high accuracy, require less memory, and consume less CPU time. They significantly outperform the existing false-positive algorithms.

Average-Case Performance of the Apriori Algorithm

The failure rate of the Apriori Algorithm is studied analytically for the case of random shoppers. The time needed by the Apriori Algorithm is determined by the number of item sets that are output (successes: item sets that occur in at least k baskets) and the number of item sets that are counted but not output (failures: item sets where all subsets of the item set occur in at least k baskets but the full set occurs in less than k baskets). The number of successes is a property of the data; no algorithm that is required to output each success can avoid doing work associated with the successes. The number of failures is a property of both the algorithm and the data.We find that under a wide range of conditions the performance of the Apriori Algorithm is almost as bad as is permitted under sophisticated worst-case analyses. In particular, there is usually a bad level with two properties: (1) it is the level where nearly all of the work is done, and (2) nearly all item sets counted are failures. Let l be the...

Large scale data mining based on data partitioning

Dealing with very large databases is one of the defining challenges in data mining research and development. Some databases are simply too large (e.g., with terabytes of data) to be processed at one time. For efficiency and space reasons, partitioning them into subsets for processing is necessary. However, since the number of itemsets in each partitioned data subset can be a combinatorial amount and each of them may be a large itemset in the original database, data mining results from these subsets can be very large in size. Therefore, the key to data partitioning is how to aggregate the results from these subsets. It is not realistic to keep all results from each subset, because the rules from one subset need to be verified for usefulness in other subsets. This article presents a model of aggregating association rules from different data subsets by weighting. In particular, the aggregation efficiency is enhanced by rule selection.

A computer program for Likert format questionnaire analysis

On college and university campuses, Likert format questionnaires are used for student evaluation of diverse aspects of courses, often with multiple items ~rouped into ~ets with a common referent (e.g., instructor, assignments, presentations, etc.). Contrary to summated rating scales which yield a total score to place a respondent somewhere on a continuum of agreement-disagreement toward the attitude being measured (Kerlinger, 1973), course evaluation questionnaires are typically analyzed to obtain respondent group means to each item or to sets of items. Moreover, the instructor examines the number and percentage of respondents selecting each al~er.native and makes comparisons among items within a set and between sets of items to determine relative strengths and weaknesses of the course. While subprograms of larger statistical analysis packages, such as SPSS (Nie, Hull, Jenkins, Steinbrenner, & Bent, 1975), will perform analyses such as those described, they are often inappropriate because they (1) require a computer system large enough to support the entire package, (2) cannot perform an analysis of individual items and sets of items. simultaneously, and (3) generate more output than IS necessary while requiring extensive variable and parameter definition. The program described in this paper performs the an~lyses typically sought for Likert format questionnam~s ~suc~ as ~ourse evaluations) while overcoming the Iimitations Imposed by larger generalized statistical analysis packages. Input. The job deck consists of the following: title card~, a. problem. parameters card, item group specification card, Item group label cardts), optional question and response choice label cards, a data format card, data deck, and a last card. The first three cards describe the problem in a text which is reproduced on the output. The problem parameters card indicates the number of respondents, the population size, the number of item sets, and user preference for optional alphanumeric labeling. The next card specifies the number of items in each set (or the total number of items if they are ungrouped) and is followed by item group labels (one per card to a maximum of 80 characters). If the user selects alphanumeric output labeling, the next cards contain the questions (one per card to a maximum of 80 characters) and the response alternatives (one per card to a maximum of 20 characters) in the same