Mining Maximal Frequent Itemsets Research Articles

Deep Web Integrated Query Interface Construction Method Based on Apriori Algorithm

Deep Web contains numerous data resources and it has been a hot topic in the database research eld. There are many researches focused on Deep Web query interface discovery, form information extraction, etc. However, a very limited amount of studies are about Deep Web integrated query interface construction till now. This paper provides an integrated interface construction method based on Apriori Algorithm. This method uses the Apriori Algorithms nature of mining maximum frequent item sets to nd the query conditions that appear frequently in one area, and accepts these query conditions as the layout in the integrated query interface. Then the method denes the control elements of the query conditions and their value ranges according to the principle that the integrated query interface must have good query translation function to every other query interface of this area. Finally, this method is used to construct integrated query interface for book area, and the result shows that the method is feasible to generate a friendly area representative integrated query interface.

Optimized Algorithm for Mining Maximum Frequent Itemsets on Association Rule

Aiming at the weakness of traditional Apriori algorithm, this paper presents MFI algorithm for mining maximum frequent itemsets on association rules. MFI algorithm scans database only once, the algorithm need not produce candidate itemsets, MFI algorithm does not use the method of iteration for each layer, MFI algorithm adopts binary bit and logic operation.The efficiency is distinctly improved in mining maximum frequent itemset.

Indexed Enhancement on GenMax Algorithm for Fast and Less Memory Utilized Pruning of MFI and CFI

The essential problem in many data mining applications is mining frequent item sets such as the discovery of association rules, patterns, and many other important discovery tasks. Fast and less memory utilization for solving the problems of frequent item sets are highly required in transactional databases. Methods for mining frequent item sets have been implemented using a prefix-tree structure, known as an FP-tree, for storing compressed information about frequent item sets which is too large to fit in memory. GenMax, a search based algorithm is used for mining maximal frequent item sets. GenMax uses a number of optimizations to prune the search space. It uses a technique called progressive focusing to perform maximal checking, and differential set propagation to perform fast frequency computation. The proposal in this paper present an improved index based enhancement on GenMax algorithm for effective fast and less memory utilized pruning of maximal frequent item sets and closed frequent item sets. The proposed model reduce the number of disk I/Os and make frequent item set mining scale to large transactional databases. Experimental results shows a comparison of improved index based GenMax and existing GenMax for efficient pruning of maximal frequent and closed frequent item sets in terms of item precision and fastness. General Terms Computer Science, Data Mining, Association Rule Mining

Max-FISM: Mining (recently) maximal frequent itemsets over data streams using the sliding window model

Frequent itemset mining from data streams is an important data mining problem with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. However, it is also a difficult problem due to the unbounded, high-speed and continuous characteristics of streaming data. Therefore, extracting frequent itemsets from more recent data can enhance the analysis of stream data. In this paper, we propose an efficient algorithm, called Max-FISM (Maximal-Frequent Itemsets Mining), for mining recent maximal frequent itemsets from a high-speed stream of transactions within a sliding window. According to our algorithm, whenever a new transaction is inserted in the current window only its maximum itemset should be inserted into a prefix tree-based summary data structure called Max-Set for maintaining the number of independent appearance of each transaction in the current window. Finally, the set of recent maximal frequent itemsets is obtained from the current Max-Set. Experimental studies show that the proposed Max-FISM algorithm is highly efficient in terms of memory and time complexity for mining recent maximal frequent itemsets over high-speed data streams.

A Bitmap Approach for Closed and Maximal Frequent Itemset Mining

Association Rule Mining (ARM) plays a fundamental role in many data mining tasks that attempt to find interesting patterns from databases, such as correlations, sequences, episodes, classifiers, clusters, etc. Frequent Itemset Mining (FIM) is one of the essential parts of ARM which is an active research area and a large number of algorithms have been developed. FIM algorithms may be depth-first or breadth-first approach. Most depth-first based approaches do not effectively address the cost of database projections. Consequently, their performance gets degraded severely as the total number of frequent itemsets in a database increases significantly. To solve this problem, a three - strategy adaptive algorithm, Bitmap Itemset Support Counting (BISC) with the closed and maximal frequent itemset is presented in this paper. The core strategy of the proposed algorithm is the usage of the closed and maximal frequent itemset detection which could be able to find least number of association rules. The proposed approach is compared with the conventional approach to evaluate the performance and accuracy. The experimental results show that the proposed approach outperforms the existing approach. Keywords: Data Mining Algorithms; Bitmap; Frequent Itemset Mining; Association Rule Mining.

A Simple but Effective Maximal Frequent Itemset Mining Algorithm over Streams

Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining. This paper considers a simple but effective algorithm for mining maximal frequent itemsets over a stream landmark. We design a compact data structure named FP-FOREST to improve an state-of-the-art algorithm INSTANT; thus, itemsets can be compressed and the support counting can be effective performed. Our experimental results show our algorithm achieves a better performance in memory cost and running time cost.

A False Negative Maximal Frequent Itemsets Mining Algorithm over Stream

Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining. This paper focuses on mining maximal frequent itemsets approximately over a stream landmark model. A false negative method is proposed based on Chernoff Bound to save the computing and memory cost. Our experimental results on a real world dataset show that our algorithm is effective and efficient.

An Improved Mining Algorithm of Maximal Frequent Itemsets

Mining maximal frequent itemsets is very important in many data mining applications. How to improve the efficiency and effectiveness of mining algorithm has become an interesting issue in the world. In this paper, we introduce a new method to solve this problem, which is based on graph theory. Firstly, the concept of directed itemsets graph and the trifurcate linked list storage structure are proposed. Secondly, the mining algorithm of maximal frequent itemsets based on this kind of structure is presented and developed. Finally, through some experimental analyses on the standard data sets, the results have demonstrated that the proposed mining algorithm can realize one time database scanning and improve time-storage efficiency a lot.

An improved association rules mining method

Mining maximal frequent itemsets is of paramount relevance in many of data mining applications. The “traditional” algorithms address this problem through scanning databases many times. The latest research has already focused on reducing the number of scanning times of databases and then decreasing the number of accessing times of I/O resources in order to improve the overall mining efficiency of maximal frequent itemsets of association rules. In this paper, we present a form of the directed itemsets graph to store the information of frequent itemsets of transaction databases, and give the trifurcate linked list storage structure of directed itemsets graph. Furthermore, we develop the mining algorithm of maximal frequent itemsets based on this structure. As a result, one realizes scanning a database only once, and improves storage efficiency of data structure and time efficiency of mining algorithm.

Algorithm for mining maximum frequent itemsets based on decreasing dimension of frequent itemset in association rules

These algorithms based on FP-tree,for mining maximal frequent pattern,have high performance but with many drawbacks.For example,they must recursively generate conditional FP-trees and many candidate maximum frequent itemsets.In order to overcome these drawbacks of the existing algorithms,an algorithm named Based on Dimensionality Reduction of Frequent Itemset(BDRFI) for mining maximal frequent patterns was put forward after the analysis of FPMax and DMFIA algorithms.The new algorithm was based on decreasing dimension of itemset.In order to enhance efficiency of superset checking,the algorithm used Digital Frequent Pattern Tree(DFP-tree) instead of FP-tree,and reduced the number of mining through prediction and pruning before mining.During the mining process,a strategy of decreasing dimension of frequent itemset was used to generate candidate frequent itemsets.The method not only reduced the number of candidate frequent itemsets but also can avoid creating conditional FP-tree separately and recursively.The experimental results show that the efficiency of BDRFI is 2-8 times as much as that of other similar algorithms.

An algorithm for maximal frequent itemset mining from large databases using bit-set representation scheme

The proposed algorithm, used for mining maximal frequent itemsets, finds interesting correlations between data in an efficient manner and can be helpful in case of large databases where large frequent itemsets contain large number of attributes. The algorithm used, works efficiently when the number of items in the large frequent itemsets is close to the number of total attributes in the dataset, or if the number of items in the large frequent itemsets is predetermined. The salient feature of the proposed approach is that it always automatically satisfies downward closure property, which states that all subsets of a frequent set must also be frequent. Owing to bit-set representation of dataset and utilization of unique list structure, the process of counting the occurrences of itemsets becomes faster, which ultimately enhances the overall mining performance.

Mining Maximal Frequent Item Sets

Data mining or knowledge discovery in databases (KDD) is a collection of exploration techniques based on advanced analytical methods and tools for handling a large amount of information. Mining association rule is a main content of data mining research at present, and emphasizes particularly is finding the relation of different items in the database. How to generate frequent item sets is the key and core. It is an important aspect in improving mining algorithm that how to decrease item set candidates in order to generate frequent item set effectively. Efficient algorithms for mining frequent items etc are crucial for mining association rules. Most existing work focuses on mining all frequent item sets (FI). However, since any subset of a frequent item set also is frequent, it is sufficient to mine only the set of maximal frequent item sets (MFI). In this paper we study the performance of existing approach, Max-Miner, for mining maximal frequent item sets. We have also developed an algorithm, called M-fp. We also present experimental results which shows that our method outperforms the existing method Max-Miner.

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.

New Policy of Maximal Frequent Itemsets in Data Stream Mining

According to the features of data streams and combined sliding window, a new algorithm A-MFI which is based on self-adjusting and orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. This algorithm which is based on basic window updates information from data stream flow fragments and scans the stream only once to gain and store it in frequent itemsets list when the data stream flows. The core idea of this algorithm: construct self-adjusting and orderly-compound FP-tree, use mixed subset pruning techniques to reduce the search space, merge nodes which has equal minsup in the same branch and compress to generate the orderly-compound FP-tree to avoid superset checking when mining maximal frequent itemsets. The experimental results show that the algorithm has higher efficiency in time and space, and also has good scalability.

Determining Attributes to Maximize Visibility of Objects

In recent years, there has been significant interest in the development of ranking functions and efficient top-k retrieval algorithms to help users in ad hoc search and retrieval in databases (e.g., buyers searching for products in a catalog). We introduce a complementary problem: How to guide a seller in selecting the best attributes of a new tuple (e.g., a new product) to highlight so that it stands out in the crowd of existing competitive products and is widely visible to the pool of potential buyers. We develop several formulations of this problem. Although the problems are NP-complete, we give several exact and approximation algorithms that work well in practice. One type of exact algorithms is based on integer programming (IP) formulations of the problems. Another class of exact methods is based on maximal frequent item set mining algorithms. The approximation algorithms are based on greedy heuristics. A detailed performance study illustrates the benefits of our methods on real and synthetic data.

Classification of human cancers based on DNA copy number amplification modeling

BackgroundDNA amplifications alter gene dosage in cancer genomes by multiplying the gene copy number. Amplifications are quintessential in a considerable number of advanced cancers of various anatomical locations. The aims of this study were to classify human cancers based on their amplification patterns, explore the biological and clinical fundamentals behind their amplification-pattern based classification, and understand the characteristics in human genomic architecture that associate with amplification mechanisms.MethodsWe applied a machine learning approach to model DNA copy number amplifications using a data set of binary amplification records at chromosome sub-band resolution from 4400 cases that represent 82 cancer types. Amplification data was fused with background data: clinical, histological and biological classifications, and cytogenetic annotations. Statistical hypothesis testing was used to mine associations between the data sets.ResultsProbabilistic clustering of each chromosome identified 111 amplification models and divided the cancer cases into clusters. The distribution of classification terms in the amplification-model based clustering of cancer cases revealed cancer classes that were associated with specific DNA copy number amplification models. Amplification patterns – finite or bounded descriptions of the ranges of the amplifications in the chromosome – were extracted from the clustered data and expressed according to the original cytogenetic nomenclature. This was achieved by maximal frequent itemset mining using the cluster-specific data sets. The boundaries of amplification patterns were shown to be enriched with fragile sites, telomeres, centromeres, and light chromosome bands.ConclusionsOur results demonstrate that amplifications are non-random chromosomal changes and specifically selected in tumor tissue microenvironment. Furthermore, statistical evidence showed that specific chromosomal features co-localize with amplification breakpoints and link them in the amplification process.

Efficient mining of maximal frequent itemsets from databases on a cluster of workstations

In this paper, we propose a new algorithm, named Distributed Max-Miner (DMM), for mining maximal frequent itemsets from databases. A frequent itemset is maximal if none of its supersets is frequent. DMM requires very low communication and synchronization overhead in distributed computing systems. DMM has the local mining phase and the global mining phase. During the local mining phase, each node mines the local database to discover the local maximal frequent itemsets, then they form a set of maximal candidate itemsets for the top-down search in the subsequent global mining phase. A new prefix-tree data structure is developed to facilitate the storage and counting of the global candidate itemsets of different sizes. This global mining phase using the prefix-tree can work with any local mining algorithm. We implemented DMM on a cluster of workstations and evaluated its performance for various cases. DMM demonstrates better performance than other sequential and parallel algorithms, and its performance is quite scalable, even when there are large maximal frequent itemsets (i.e., long patterns) in databases.

Approximate mining of maximal frequent itemsets in data streams with different window models

A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid rate. Mining data streams is more difficult than mining static databases because the huge, high-speed and continuous characteristics of streaming data. In this paper, we propose a new one-pass algorithm called DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets), which mines the set of all maximal frequent itemsets in landmark windows over data streams. A new summary data structure called summary frequent itemset forest (abbreviated as SFI-forest) is developed for incremental maintaining the essential information about maximal frequent itemsets embedded in the stream so far. Theoretical analysis and experimental studies show that the proposed algorithm is efficient and scalable for mining the set of all maximal frequent itemsets over the entire history of the data streams.

Distributed Mining of Maximal Frequent Itemsets on a Data Grid System

In this paper, we propose a new algorithm, named Grid-based Distributed Max-Miner (GridDMM), for mining maximal frequent itemsets from databases on a Data Grid. A frequent itemset is maximal if none of its supersets is frequent. GridDMM is specifically suitable for use in Grid environments due to low communication and synchronization overhead. GridDMM consists of a local mining phase and a global mining phase. During the local mining phase, each node mines the local database to discover the local maximal frequent itemsets, then they form a set of maximal candidate itemsets for the top-down search in the subsequent global mining phase. A new prefix-tree data structure is developed to facilitate the storage and counting of the global candidate itemsets of different sizes. We built a Data Grid system on a cluster of workstations using the open-source Globus Toolkit, and evaluated the GridDMM algorithm in terms of performance, scalability, and the overhead of communication and synchronization. GridDMM demonstrates better performance than other sequential and parallel algorithms, and its performance is scalable in terms of the database size and the number of nodes.

Computational aspects of mining maximal frequent patterns

In this paper we study the complexity-theoretic aspects of mining maximal frequent patterns, from the perspective of counting the number of all distinct solutions. We present the first formal proof that the problem of counting the number of maximal frequent itemsets in a database of transactions, given an arbitrary support threshold, is #P-complete, thereby providing theoretical evidence that the problem of mining maximal frequent itemsets is NP-hard. We also extend our complexity analysis to other similar data mining problems that deal with complex data structures, such as sequences, trees, and graphs. We investigate several variants of these mining problems in which the patterns of interest are subsequences, subtrees, or subgraphs, and show that the associated problems of counting the number of maximal frequent patterns are all either #P-complete or #P-hard.