Frequent Closed Itemset Mining Research Articles

Multi-Sized cumulative Summary Structure Driven Light Weight in Frequent Closed Itemset Mining to Increase High Utility

Multi-Sized cumulative Summary Structure Driven Light Weight in Frequent Closed Itemset Mining to Increase High Utility

Organization Preference Knowledge Acquisition of Multi-Platform Aircraft Mission System Utilizing Frequent Closed Itemset Mining

Organization preference knowledge is critical to enhancing the intelligence and efficiency of the multi-platform aircraft mission system (MPAMS), particularly the collaboration tactics of task behaviors, platform types, and mount resources. However, it is challenging to extract such knowledge concisely, which is buried in massive historical data. Therefore, this paper proposes an innovative data-driven approach via frequent closed itemset mining (FCIM) algorithm to discover valuable MPAMS organizational knowledge. The proposed approach addresses the limitations of poor effectiveness and low mining efficiency for the previously discovered knowledge. To ensure the knowledge effectiveness, this paper designs a multi-layer knowledge discovery framework from the system-of-systems perspective, allowing to discover more systematic knowledge than traditional frameworks considering an isolated layer. Additionally, the MPAMS’s contextual capability reflecting the decision motivation is integrated into the knowledge representation, making the knowledge more intelligible to decision-makers. Further, to ensure mining efficiency, the knowledge mining process is accelerated by designing an itemset storage structure and three pruning strategies for FCIM. The simulation of 1100 air-to-sea assault scenarios has provided abundant knowledge with high interpretability. The performance superiority of the proposed approach is thoroughly verified by comparative experiments. The approach provides guidance and insights for future MPAMS development and organization optimization.

Unsupervised Derivation of Keyword Summary for Short Texts

Automatically summarizing a group of short texts that mainly share one topic is a fundamental task in many applications, e.g., summarizing the main symptoms for a disease based on a group of medical texts that are usually short, i.e., tens of words. Conventional unsupervised short text summarization techniques tend to find the most representative short text document. However, they may cause privacy issues, e.g., personal information in the medical texts may be exposed. Moreover, compared with the complete short text where some unimportant words may exist, a summary consisting of only a few keywords is more preferable by the user due to its clear and concise form. Due to the above reasons, in this article, we aim to solve the problem of unsupervised derivation of keyword summary for short texts. Existing keyword extraction methods such as Latent Dirichlet Allocation cannot be applied to solve this problem, since (1) the ordering relations among the extracted keywords are ignored, which causes troubles for people to capture the main idea of the event, and (2) short texts contain limited context, which makes it hard to find the optimal words for semantic coverage. Hence, we propose a simple but yet effective method named Frequent Closed Wordsets Ranking (FCWRank) to derive the keyword summary from a short text cluster. FCWRank is an unsupervised method that builds on the idea of frequent closed itemset mining in transaction database. FCWRank first mines all frequent closed wordsets from a cluster of short texts and then selects the most important wordset based on an importance model where the similarity between closed wordsets and the relation between the closed wordset and the short text document are considered simultaneously. To make the keywords within the wordset more understandable, FCWRank further unfolds the semantics behind them by sorting them. Experiments on real-world short text collections show that FCWRank outperforms the state-of-the-art baselines in terms of Recall-Oriented Understudy for Gisting Evaluation-Longest common subsequence F1, precision and recall scores.

Fast and effective cluster-based information retrieval using frequent closed itemsets

Document Information retrieval consists of finding the documents in a collection of documents that are the most relevant to a user query. Information retrieval techniques are widely-used by organizations to facilitate the search for information. However, applying traditional information retrieval techniques is time consuming for large document collections. Recently, cluster-based information retrieval approaches have been developed. Although these approaches are often much faster than traditional approaches for processing large document collections, the quality of the documents retrieved by cluster-based approaches is often less than that of traditional approaches. To address this drawback of cluster-based approaches, and improve the performance of information retrieval both in terms of runtime and quality of retrieved documents, this paper proposes a new cluster-based information retrieval approach named ICIR (Intelligent Cluster-based Information Retrieval). The proposed approach combines k-means clustering with frequent closed itemset mining to extract clusters of documents and find frequent terms in each cluster. Patterns discovered in each cluster are then used to select the most relevant document clusters to answer each user query. Four alternative heuristics are proposed to select the most relevant clusters, and two alternative heuristics for choosing documents in the selected clusters. Thus, eight versions of the proposed approach are obtained. To validate the proposed approach, extensive experiments have been carried out on well-known document collections. Results show that the designed approach outperforms traditional and cluster-based information retrieval approaches both in terms of execution time and quality of the returned documents.

SAT-Based Data Mining

Several propositional satisfiability (SAT) based encodings have been proposed to deal with various data mining problems including itemset and sequence mining problems. This research issue allows to model data mining problems in a declarative way, while exploiting efficient SAT solving techniques. In this paper, we overview our contributions on the application of propositional satisfiability (SAT) to model and solve itemset mining tasks. We first present a SAT based encoding of frequent closed itemset mining task as a propositional formula whose models corresponds to the patterns to be mined. Secondly, we show that some data mining constraints can be avoided by reformulation. We illustrate this issue by reformulating the closeness constraint using the notion of minimal models. Finally, we also addressed the scalability issue, one of the most important challenge of these nice declarative framework. To this end, we proposed a complete partition based approach whose aim is to avoid encoding the whole database as a single formula. Using a partition on the set of items, our new approach leads to several propositional formulas of reasonable size. The experimental evaluation on several known datasets shows huge improvements in comparison to the direct approach without partitioning, while reducing significantly the performance gap with respect to specialized algorithms.

A Parallel MapReduce Algorithm to Efficiently Support Itemset Mining on High Dimensional Data

Abstract In today's world, large volumes of data are being continuously generated by many scientific applications, such as bioinformatics or networking. Since each monitored event is usually characterized by a variety of features, high-dimensional datasets have been continuously generated. To extract value from these complex collections of data, different exploratory data mining algorithms can be used to discover hidden and non-trivial correlations among data. Frequent closed itemset mining is an effective but computational expensive technique that is usually used to support data exploration. Thanks to the spread of distributed and parallel frameworks, the development of scalable approaches able to deal with the so called Big Data has been extended to frequent itemset mining. Unfortunately, most of the current algorithms are designed to cope with low-dimensional datasets, delivering poor performances in those use cases characterized by high-dimensional data. This work introduces PaMPa-HD, a MapReduce-based frequent closed itemset mining algorithm for high dimensional datasets. An efficient solution has been proposed to parallelize and speed up the mining process. Furthermore, different strategies have been proposed to easily configure the algorithm parameter. The experimental results, performed on real-life high-dimensional use cases, show the efficiency of the proposed approach in terms of execution time, load balancing and robustness to memory issues.

Mining constraint based association rules on Pest data using closed frequent items

One of the very important tasks in data mining is discovering association rules. Agricultural data are voluminous and thus generating all frequent item sets requires large memory as well as takes high computational time. A new approach for the analysis of soybean pest data has been proposed using an algorithm of frequent closed item sets to reduce computational time, and to overcome memory explosion problems. Frequent closed patterns and redundant rules are identified using frequent closed item set mining with intervals. The algorithm produces highly important or redundant rules compared with traditional algorithms. Proposed algorithm requires less memory and as well takes less time compared to Apriori algorithm that also generates frequent closed item sets in such dense dataset.

Novel techniques and an efficient algorithm for closed pattern mining

In this paper we show that frequent closed itemset mining and biclustering, the two most prominent application fields in pattern discovery, can be reduced to the same problem when dealing with binary (0–1) data. FCPMiner, a new powerful pattern mining method, is then introduced to mine such data efficiently. The uniqueness of the proposed method is its extendibility to non-binary data. The mining method is coupled with a novel visualization technique and a pattern aggregation method to detect the most meaningful, non-overlapping patterns. The proposed methods are rigorously tested on both synthetic and real data sets.

Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data

During the last decade various algorithms have been developed and proposed for discovering overlapping clusters in high-dimensional data. The two most prominent application fields in this research, proposed independently, are frequent itemset mining (developed for market basket data) and biclustering (applied to gene expression data analysis). The common limitation of both methodologies is the limited applicability for very large binary data sets. In this paper we propose a novel and efficient method to find both frequent closed itemsets and biclusters in high-dimensional binary data. The method is based on simple but very powerful matrix and vector multiplication approaches that ensure that all patterns can be discovered in a fast manner. The proposed algorithm has been implemented in the commonly used MATLAB environment and freely available for researchers.

A fast compound algorithm for mining generators, closed itemsets, and computing links between equivalence classes

In pattern mining and association rule mining, there is a variety of algorithms for mining frequent closed itemsets (FCIs) and frequent generators (FGs), whereas a smaller part further involves the precedence relation between FCIs. The interplay of these three constructs and their joint computation have been studied within the formal concept analysis (FCA) field yet none of the proposed algorithms is scalable. In frequent pattern mining, at least one suite of efficient algorithms has been designed that exploits basically the same ideas and follows the same overall computational schema. Based on an in-depth analysis of the aforementioned interplay that is rooted in a fundamental duality from hypergraph theory, we propose a new schema that should enable for a more parsimonious computation. We exemplify the new schema in the design of Snow-Touch, a concrete FCI/FG/precedence miner that reuses an existing algorithm, Charm, for mining FCIs, and completes it with two original methods for mining FGs and precedence, respectively. The performance of Snow-Touch and of its closest competitor, Charm-L, were experimentally compared using a large variety of datasets. The outcome of the experimental study suggests that our method outperforms Charm-L on dense data while on sparse one the trend is reversed. Furthermore, we demonstrate the usefulness of our method and the new schema through an application to the analysis of a genome dataset. The initial results reported here confirm the capacity of the method to focus on significant associations.

EBPA: An efficient data structure for frequent closed itemset mining

In closed itemset mining, the process of mining from a large transaction database directly often leads to inefficient space and time. Practically, many data structures were proposed to maintain valuable data for frequent closed itemset mining (FCIM), while each data structure has its own advantages and disadvantages. In recent study, a collaboration of array, bitmap, and prefix tree was proposed to gain advantages of those basic data structures by reducing the computing time of the FCIM. That collaboration can save space over that of the original prefix tree, which requires extra space for (m-1) parent-child pointers and its corresponding hashing table (in each tree-node). However, the extra sorting all transactions and merging repeated transactions are required before constructing the prefix tree. Therefore, this paper presents the improved collaboration data structure, called the EBPA (Efficient Bitmap-Prefix-tree Array), with the efficient (parent-child) access in O(1) by using a (temporary) pointer array (without space for hashing) and not require the extra sorting transactions. In system performance evaluation, experimental results showed that the response time of our EBPA-based FCIM mining outperforms over that of the existing collaboration-based FCIM approach.

A sliding window based algorithm for frequent closed itemset mining over data streams

Frequent pattern mining over data streams is an important problem in the context of data mining and knowledge discovery. Mining frequent closed itemsets within sliding window instead of complete set of frequent itemset is very interesting since it needs a limited amount of memory and processing power. Moreover, handling concept change within a compact set of closed patterns is faster. However, it requires flexible and efficient data structures as well as intuitive algorithms. In this paper, we have introduced an effective and efficient algorithm for closed frequent itemset mining over data streams operating in the sliding window model. This algorithm uses a novel data structure for storing transactions of the window and corresponding frequent closed itemsets. Moreover, the support of a new frequent closed itemset is efficiently computed and an old pattern is removed from the monitoring set when it is no longer frequent closed itemset. Extensive experiments on both real and synthetic data streams show that the proposed algorithm is superior to previously devised algorithms in terms of runtime and memory usage.

DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets

Frequent closed itemsets (FCI) play an important role in pruning redundant rules fast. Therefore, a lot of algorithms for mining FCI have been developed. Algorithms based on vertical data formats have some advantages in that they require scan databases once and compute the support of itemsets fast. Recent years, BitTable (Dong & Han, 2007) and IndexBitTable (Song, Yang, & Xu, 2008) approaches have been applied for mining frequent itemsets and results are significant. However, they always use a fixed size of Bit-Vector for each item (equal to number of transactions in a database). It leads to consume more memory for storage Bit-Vectors and the time for computing the intersection among Bit-Vectors. Besides, they only apply for mining frequent itemsets, algorithm for mining FCI based on BitTable is not proposed. This paper introduces a new method for mining FCI from transaction databases. Firstly, Dynamic Bit-Vector (DBV) approach will be presented and algorithms for fast computing the intersection between two DBVs are also proposed. Lookup table is used for fast computing the support (number of bits 1 in a DBV) of itemsets. Next, subsumption concept for memory and computing time saving will be discussed. Finally, an algorithm based on DBV and subsumption concept for mining frequent closed itemsets fast is proposed. We compare our method with CHARM, and recognize that the proposed algorithm is more efficient than CHARM in both the mining time and the memory usage.

Maintaining frequent closed itemsets over a sliding window

In this paper, we study the incremental update of Frequent Closed Itemsets (FCIs) over a sliding window in a high-speed data stream. We propose the notion of semi-FCIs, which is to progressively increase the minimum support threshold for an itemset as it is retained longer in the window, thereby drastically reducing the number of itemsets that need to be maintained and processed. We explore the properties of semi-FCIs and observe that a majority of the subsets of a semi-FCI are not semi-FCIs and need not be updated. This finding allows us to devise an efficient algorithm, IncMine, that incrementally updates the set of semi-FCIs over a sliding window. We also develop an inverted index to facilitate the update process. Our empirical results show that IncMine achieves significantly higher throughput and consumes less memory than the state-of-the-art streaming algorithms for mining FCIs and FIs. IncMine also attains high accuracy of 100% precision and over 93% recall.

Frequent Closed Item Set Mining Based on Zero-suppressed BDDs

Frequent Closed Item Set Mining Based on Zero-suppressed BDDs

Frequent Closed Item Set Mining Based on Zero-suppressed BDDs

keywords: data mining, item set, BDD, ZBDD, closed patternSummaryFrequent item set mining is one of the fundamental techniques for knowledge discovery and data min-ing. In the last decade, a number of efficient algorithms for frequent item set mining have been presented,but most of them focused on just enumerating the item set patterns which satisfy the given conditions, andit was a different matter how to store and index the result of patterns for efficient data analysis. Recently,we proposed a fast algorithm of extracting all frequent item set patterns from transaction databases andsimultaneously indexing the result of huge patterns using Zero-suppressed BDDs (ZBDDs). That method,ZBDD-growth, is not only enumerating/listing the patterns efficiently, but also indexing the output datacompactly on thememory to be analyzed with variousalgebraic operations. In this paper, we present a vari-ation of ZBDD-growth algorithm to generate frequent closed item sets. This is a quite simple modificationof ZBDD-growth, and additional computation cost is relatively small compared with the original algorithmfor generating all patterns. Our method can conveniently be utilized in the environment of ZBDD-basedpattern indexing.