Set Of Frequent Patterns Research Articles

Identification of Multidimensional Relationship among Item Sets using Association Rules

In recent days, there is more interest in discovering multi dimensional relationship among item sets rather than frequent pattern sets for applications in specific domains viz. identification of irregularities in stock marketing, assessing the causes in certain diseases, identifying irregularities in farming system etc. This paper focuses on the mining of multi dimensional relationship among various item sets. An efficient algorithm to identify multi dimensional relationship in Inter Disciplined Independent Variables (IDIV) and dependent variable has been proposed. The effectiveness of the algorithm has been assessed on real world data set related to socio-economic conditions of farming system. General Terms Data mining task discovering multidimensional relationship among item sets. Keyword Multidimensional relationship, association rule, support, confidence.

An Efficient Frequent Itemset Mining Method over High-speed Data Streams

Frequent itemset mining over sliding window is an interesting problem and has a large number of applications. Sliding window is a widely used model for frequent itemset mining over data streams due to its capability to handle concept drift, its bounded memory and its low processing time. A sliding window-based algorithm requires an efficient data structure that can be updated as fast as possible when inserting and deleting transactions. Moreover, an innovative computing method is needed to provide the set of frequent patterns (FPs) with a little delay after the user issues a request for the mining results within a window. In this study, an efficient representation of the sliding window named blocked bit sequence is introduced which is aimed to store and maintain the content of the window. Moreover, by a novel technique this representation is exploited for efficiently extracting the set of FPs within the current window. Experimental evaluations on both real-life and synthetic data streams show that the proposed approach is faster than recently proposed algorithms in different phases of data stream mining.

An Approach to Frequent Pattern Discovery from Gene Expression Data Using PSO Variants

Pattern mining has always attracted a huge attention for generation of large amount of patterns and association between them. Though it's one of the major data mining tasks but it has always been a time consuming process as a large scale of patterns and associations rules gets generated. To reduce the time of consumption it was preferable to discretize the data matrix in the range of 0 to 1 and for this the fuzzy the membership function has been used which is quite simple in its concept and strategy. Owing to the concept of fuzzy logic, certain evolutionary algorithms (EAs) also gained popularity to optimize the process of mining patterns from the fuzzy sets. For this, Particle swarm optimization (PSO) was used which is supposed to provide better results as compared to other EA like genetic algorithm, ant colony optimization etc. But it was found that there are certain versions of PSO that provided much better results than the standard PSO algorithm. In this paper, the gene expression data set was fuzzified for the purpose of discretization in the range of 0 to 1. A Frequent Pattern (FP) growth algorithm was used to generate set of frequent patterns. These patterns were used as the initial population and the mean squared residue (MSR) score was used as an evaluation criteria. Fully Informed Particle Swarm Optimization (FIPSO), Dynamic Multi Swarm Particle Swarm Optimization (DMS-PSO), Comprehensive Learning Particle Swarm Optimization (CLPSO), Vector Evaluated Particle Swarm Optimization (VEPSO) etc are the certain versions of PSO that were used and they provided much better results as compared to standard PSO algorithm. But the VEPSO algorithm outperformed the other three algorithms in terms of generation of best individual frequent patterns, runtime and the volume of mean squared residue (lower the MSR score the better is the quality of the patterns).

A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models.

BackgroundRemote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM).ResultsWe use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function.ConclusionsThe strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.

Sweeping the disjunctive search space towards mining new exact concise representations of frequent itemsets

Concise (or condensed) representations of frequent patterns follow the minimum description length (MDL) principle, by providing the shortest description of the whole set of frequent patterns. In this work, we introduce a new exact concise representation of frequent itemsets. This representation is based on an exploration of the disjunctive search space. The disjunctive itemsets convey information about the complementary occurrence of items in a dataset. A novel closure operator is then devised to suit the characteristics of the explored search space. The proposed operator aims at mapping many disjunctive itemsets to a unique one, called a disjunctive closed itemset. Hence, it permits to drastically reduce the number of handled itemsets within the targeted re-presentation. Interestingly, the proposed representation offers direct access to the disjunctive and negative supports of frequent itemsets while ensuring the derivation of their exact conjunctive supports. We conclude from the experimental results reported and discussed here that our representation is effective and sound in comparison with different other concise representations.

CRMS: An Algorithm of Classification Rule Mining Based on Multiple Supports

The paper presents an algorithm CRMS of classification rule mining based on multi-support, in which the frequent classification item-set tree is adopted to organize the frequent pattern sets, and array to figure the classification projected transaction subsets. The multi-support method is applied in CRMS due to the unevenly distributed classification patterns. The CRMS uses the breath first strategy assisted by the depth first strategy, and adopts pseudo projection, which makes it unnecessary to scan the database and construct the projected transaction subsets repeatedly, therefore the memory and time cost is very low, and the projecting-efficiency and scalability are higher. The CRMS algorithm can be used in the basket analysis, association rule mining for consumption behavior in the retailing industry, which supports the product layout, buying recommendation and the sale promotion.

Efficient algorithms for incremental maintenance of closed sequential patterns in large databases

Recent study shows that mining compact frequent patterns (such as closed patterns and compressed patterns) can alleviate the interpretability and efficiency problem encountered by traditional frequent pattern mining methods. Compact frequent patterns keep exact or approximate supports of a complete set of frequent patterns, and the number of them is often orders of magnitude smaller. Several efficient algorithms have been proposed to mine compact sequential patterns. However, sequence databases are not always static. Sequences (or items) are often added to and deleted from databases. A slight change made on a database may lead to the change of compact patterns. Mining from scratch is very time-consuming and thus infeasible. In this paper, we explore how to efficiently maintain closed sequential patterns in a dynamic sequence database environment. A compact structure CSTree is designed to keep closed sequential patterns, and its nice properties are carefully studied. Two efficient algorithms, IMCS A and IMCS D , are developed to maintain the CSTree upon incremental update. The algorithms make full use of the properties of CSTree to find nodes whose states are obsolete and avoid unnecessary node extension and closure checking operations to accelerate the incremental update process. A thorough experimental study on various real and synthetic datasets shows that the proposed algorithms outperform the state-of-the-art algorithms – PrefixSpan, CloSpan, BIDE and a recently proposed incremental mining algorithm IncSpan by about a factor of 4 to more than an order of magnitude.

Mining top- k Hot Melody Structures over online music query streams

Online mining of frequent patterns from music data is one of the most important research issues of multimedia data mining. Most previous studies require the specification of a min_ support threshold and aim at mining a complete set of frequent patterns satisfying min_ support. However, in practice, it is difficult for users to provide an appropriate value of min_ support threshold. In this paper, we propose a new problem of multimedia data mining: online mining of top- k melody structures of length no less than min_ l, where k is the desired number of hot melody structures to be mined and min_ l is the minimal length of each melody structure. An efficient single-pass algorithm, called top- k-HMS (top- k Hot Melody Structures), is developed for mining such melody structures without min_ support. In the framework of top- k-HMS algorithm, a new summary data structure, called TKM-list (top- k melody list) is developed to maintain the essential information about the top- k hot melody structures from the current melody sequence streams. Experimental studies show that the proposed top- k-HMS algorithm is an efficient one-pass method for mining the set of top- k Hot Melody Structures over a continuous stream of melody sequences.

Efficient discovery of frequent continuities by projected window list technology

Mining frequent patterns is a fundamental problem in data mining research. A continuity is a kind of causal relationship which describes a definite temporal factor with exact position between the records. Since continuities break the boundaries of records, the number of potential patterns will increase drastically. An alternative approach is to mine compressed or closed frequent continuities (CFC). Mining CFCs has the same power as mining the complete set of frequent patterns, while substantially reducing redundant rules to be generated and increasing the effectiveness of mining. In this paper, we propose a method called projected window list (PWL) technology for the mining of frequent continuities. We present a series of frequent continuity mining algorithms, including PROWL+, COCOA and ClosedPROWL. Experimental evaluation shows that our algorithm is more efficient than previously works.

Semantic annotation of frequent patterns

Using frequent patterns to analyze data has been one of the fundamental approaches in many data mining applications. Research in frequent pattern mining has so far mostly focused on developing efficient algorithms to discover various kinds of frequent patterns, but little attention has been paid to the important next step—interpreting the discovered frequent patterns. Although the compression and summarization of frequent patterns has been studied in some recent work, the proposed techniques there can only annotate a frequent pattern with nonsemantical information (e.g., support), which provides only limited help for a user to understand the patterns. In this article, we study the novel problem of generating semantic annotations for frequent patterns. The goal is to discover the hidden meanings of a frequent pattern by annotating it with in-depth, concise, and structured information. We propose a general approach to generate such an annotation for a frequent pattern by constructing its context model, selecting informative context indicators, and extracting representative transactions and semantically similar patterns. This general approach can well incorporate the user's prior knowledge, and has potentially many applications, such as generating a dictionary-like description for a pattern, finding synonym patterns, discovering semantic relations, and summarizing semantic classes of a set of frequent patterns. Experiments on different datasets show that our approach is effective in generating semantic pattern annotations.

Statistical supports for mining sequential patterns and improving the incremental update process on data streams

Recently, the knowledge extraction community takes a closer look at new models where data arrive in timely manner like a fast and continuous flow, i.e. data streams. As only a part of the stream can be stored, mining data streams for sequential patterns and updating previously found frequent patterns need to cope with uncertainty. In this paper, we introduce a new statistical approach which biases the initial support for sequential patterns. This approach holds the advantage to maximize either the precision or the recall, as chosen by the user, and limit the degradation of the other criterion. Moreover, these statistical supports help building statistical borders which are the relevant sets of frequent patterns to use into an incremental mining process. From the statistical standpoint, theoretical results show that the technique is not far from the optimum. Experiments performed on sequential patterns demonstrate the interest of this approach and the potential of such techniques.

Tight upper bounds on the number of candidate patterns

In the context of mining for frequent patterns using the standard levelwise algorithm, the following question arises: given the current level and the current set of frequent patterns, what is the maximal number of candidate patterns that can be generated on the next level? We answer this question by providing tight upper bounds, derived from a combinatorial result from the sixties by Kruskal and Katona. Our result is useful to secure existing algorithms from a combinatorial explosion of the number of candidate patterns.

TSP: Mining top-k closed sequential patterns

Sequential pattern mining has been studied extensively in the data mining community. Most previous studies require the specification of a min_support threshold for mining a complete set of sequential patterns satisfying the threshold. However, in practice, it is difficult for users to provide an appropriate min_support threshold. To overcome this difficulty, we propose an alternative mining task: mining top-k frequent closed sequential patterns of length no less than min_l, where k is the desired number of closed sequential patterns to be mined and min_l is the minimal length of each pattern. We mine the set of closed patterns because it is a compact representation of the complete set of frequent patterns. An efficient algorithm, called TSP, is developed for mining such patterns without min_support. Starting at (absolute) min_support=1, the algorithm makes use of the length constraint and the properties of top-k closed sequential patterns to perform dynamic support raising and projected database pruning. Our extensive performance study shows that TSP has high performance. In most cases, it outperforms the efficient closed sequential pattern-mining algorithm, CloSpan, even when the latter is running with the best tuned min_support threshold. Thus, we conclude that, for sequential pattern mining, mining top-k frequent closed sequential patterns without min_support is more preferable than the traditional min_support-based mining.

Couvertures parfaites des motifs fréquents

The extraction of frequent patterns often yields extremely voluminous results which are difficult to handle. Computing a concise representation or cover of the frequent pattern set is an alternative investigated by various approaches. The work presented in this article fits in a similar trend. We introduce the concept of essential pattern and propose a new cover based on this concept. Such a cover makes it possible to decide whether any pattern is frequent or not, to compute its frequency and, in contrast with related work, to infer its disjunction and negation frequencies. A levelwise algorithm computing the essential patterns is proposed. The experiments show that when the number of frequent patterns is very high (strongly correlated data), the defined cover is significantly more reduced than the cover considered until now as minimal: the frequent closed patterns.

Efficient incremental maintenance of frequent patterns with FP-tree

Mining frequent patterns has been studied popularly in data mining area. However, little work has been done on mining patterns when the database has an influx of fresh data constantly. In these dynamic scenarios, efficient maintenance of the discovered patterns is crucial. Most existing methods need to scan the entire database repeatedly, which is an obvious disadvantage. In this paper, an efficient incremental mining algorithm, Incremental-Mining (IM), is proposed for maintenance of the frequent patterns when new incremental data come. Based on the frequent pattern tree (FP-tree) structure, IM gives a way to make the most of the things from the previous mining process, and requires scanning the original data once at most. Furthermore, IM can identify directly the differential set of frequent patterns, which may be more informative to users. Moreover, IM can deal with changing thresholds as well as changing data, thus provide a full maintenance scheme. IM has been implemented and the performance study shows it outperforms three other incremental algorithms: FUP, DB-tree and re-running frequent pattern growth (FP-growth).

Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns. In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.