Approximate Mining Algorithm Research Articles

Efficient frequent itemset mining methods over time-sensitive streams

Stream data arrives dynamically and rapidly, and the characteristics cannot be reflected by the traditional transaction-based sliding window; thus, the mining results are inaccurate. This paper focuses on this problem and constructs a timestamp-based sliding window model, which can be further converted into a transaction-based sliding window. Based on this model, an extended enumeration tree is developed to incrementally maintain the essential information. In our proposed frequent itemset mining algorithm, we introduce the type transforming bound to dynamically classify the itemsets into categories; thus, certain itemset processing can be deferred or ignored, that is, an itemset will not be handled unless its type transforming bounds reach a threshold; as a result, the computational pruning can be conducted. Nevertheless, it only guarantees the conditions to obtain accurate results, and thus cannot achieve the best performance. This problem is further improved in our approximate mining algorithm, in which we propose a heuristic rule-based strategy. Additionally, it can save more computational cost with a tolerable mining error. Theoretical analysis and experimental studies demonstrate that our proposed algorithms have high accuracy, spend less computational time and memory, and significantly outperform the baseline method and state-of-the-art algorithms.

확률 기법에 기반한 근접 빈발 패턴 마이닝 기법의 성능평가

근접 빈발 패턴 마이닝은 향상된 효율성을 위해 정확한 패턴보다 허용되는 범위 안에서 근접 빈발 패턴을 마이닝한다. 데이터베이스의 크기가 증대함에 따라 거대한 데이터베이스를 처리하기 위해서 더 빠른 마이닝 기법이 필요하게 되고 있다. 또한, 노이지나 데이터의 다양성 때문에 패턴을 마이닝 하는 것에 대한 정확한 결과를 찾기가 더 어렵다. 이러한 경우들에 대해, 근접 빈발 패턴 마이닝을 함으로 실행시간, 메모리 사용량, 그리고 확장성의 관점에서 더 효율적인 마이닝을 수행할 수 있다. 이 논문에서는 확률 기법에 근간한 근접 패턴 마이닝 알고리즘에 대한 특성을 살펴보고 척도가 되는 확률 기법에 기반한 근접 패턴 마이닝 알고리즘에 대해 성능 평가를 한다. 최종적으로 성능의 향상을 위해 테스트 결과를 분석한다. Approximate Frequent pattern mining is to find approximate patterns, not exact frequent patterns with tolerable variations for more efficiency. As the size of database increases, much faster mining techniques are needed to deal with huge databases. Moreover, it is more difficult to discover exact results of mining patterns due to inherent noise or data diversity. In these cases, by mining approximate frequent patterns, more efficient mining can be performed in terms of runtime, memory usage and scalability. In this paper, we study the characteristics of an approximate mining algorithm based on probabilistic technique and run performance evaluation of the efficient approximate frequent pattern mining algorithm. Finally, we analyze the test results for more improvement.

Mining frequent subgraphs over uncertain graph databases under probabilistic semantics

Frequent subgraph mining has been extensively studied on certain graph data. However, uncertainty is intrinsic in graph data in practice, but there is very few work on mining uncertain graph data. This paper focuses on mining frequent subgraphs over uncertain graph data under the probabilistic semantics. Specifically, a measure called $${\varphi}$$ -frequent probability is introduced to evaluate the degree of recurrence of subgraphs. Given a set of uncertain graphs and two real numbers $${0 < \varphi, \tau < 1}$$ , the goal is to quickly find all subgraphs with $${\varphi}$$ -frequent probability at least ?. Due to the NP-hardness of the problem and to the #P-hardness of computing the $${\varphi}$$ -frequent probability of a subgraph, an approximate mining algorithm is proposed to produce an $${(\varepsilon, \delta)}$$ -approximate set ? of "frequent subgraphs", where $${0 < \varepsilon < \tau}$$ is error tolerance, and 0 < ? < 1 is a confidence bound. The algorithm guarantees that (1) any frequent subgraph S is contained in ? with probability at least ((1 ? ?) /2) s , where s is the number of edges in S; (2) any infrequent subgraph with $${\varphi}$$ -frequent probability less than $${\tau - \varepsilon}$$ is contained in ? with probability at most ?/2. The theoretical analysis shows that to obtain any frequent subgraph with probability at least 1 ? Δ, the input parameter ? of the algorithm must be set to at most $${1 - 2 (1 - \Delta)^{1 / \ell_{\max}}}$$ , where 0 < Δ < 1, and ? max is the maximum number of edges in frequent subgraphs. Extensive experiments on real uncertain graph data verify that the proposed algorithm is practically efficient and has very high approximation quality. Moreover, the difference between the probabilistic semantics and the expected semantics on mining frequent subgraphs over uncertain graph data has been discussed in this paper for the first time.

Mining Frequent Subgraph Patterns from Uncertain Graph Data

In many real applications, graph data is subject to uncertainties due to incompleteness and imprecision of data. Mining such uncertain graph data is semantically different from and computationally more challenging than mining conventional exact graph data. This paper investigates the problem of mining uncertain graph data and especially focuses on mining frequent subgraph patterns on an uncertain graph database. A novel model of uncertain graphs is presented, and the frequent subgraph pattern mining problem is formalized by introducing a new measure, called expected support. This problem is proved to be NP-hard. An approximate mining algorithm is proposed to find a set of approximately frequent subgraph patterns by allowing an error tolerance on expected supports of discovered subgraph patterns. The algorithm uses efficient methods to determine whether a subgraph pattern can be output or not and a new pruning method to reduce the complexity of examining subgraph patterns. Analytical and experimental results show that the algorithm is very efficient, accurate, and scalable for large uncertain graph databases. To the best of our knowledge, this paper is the first one to investigate the problem of mining frequent subgraph patterns from uncertain graph data.

Approximate mining of frequent patterns on streams

Many critical applications, like intrusion detection or stock market analysis, require a nearly immediate result based on a continuous and infinite stream of data. In most cases finding an exact solution is not compatible with limited availability of resources and real time constraints, but an approximation of the exact result is enough for most purposes. This paper introduces a new algorithm for approximate mining of frequent itemsets from streams of transactions using a limited amount of memory. The proposed algorithm is based on the computation of frequent itemsets in recent data and an effective method for inferring the global support of previously infrequent itemsets. Both upper and lower bounds on the support of each pattern found are returned along with the interpolated support. An extensive experimental evaluation shows that ${AP}_{stream}$, the proposed algorithm, yields a good approximation of the exact global result considering both the set of patterns found and their supports.