Approximate Sequential Patterns Research Articles

GASP: Graph-based Approximate Sequential Pattern Mining for Electronic Health Records.

Sequential pattern mining can be used to extract meaningful sequences from electronic health records. However, conventional sequential pattern mining algorithms that discover all frequent sequential patterns can incur a high computational and be susceptible to noise in the observations. Approximate sequential pattern mining techniques have been introduced to address these shortcomings yet, existing approximate methods fail to reflect the true frequent sequential patterns or only target single-item event sequences. Multi-item event sequences are prominent in healthcare as a patient can have multiple interventions for a single visit. To alleviate these issues, we propose GASP, a graph-based approximate sequential pattern mining, that discovers frequent patterns for multi-item event sequences. Our approach compresses the sequential information into a concise graph structure which has computational benefits. The empirical results on two healthcare datasets suggest that GASP outperforms existing approximate models by improving recoverability and extracts better predictive patterns.

Content Based Indexing of Music Objects Using Approximate Sequential Patterns

Content Based Indexing of Music Objects Using Approximate Sequential Patterns

Mining approximate sequential patterns with gaps

Time series data are found in diverse fields including, science, business, medicine and engineering. In this paper, we consider sequential pattern mining for categorical time series data that contain multiple independent time-series. Frequent patterns are considered important in a variety of applications. However, it is common for data to contain noise, and/or for the source process to have considerable variability. Conventional sequential pattern mining methods that use exact matching address, some but not all of these difficulties. Two general approaches used in previous studies to mine sequential patterns in data with noise are distance-based clustering and hidden Markov models. While these approaches are useful in mining frequent sequential patterns in noisy data, we further propose a framework (MWASP: multiple-width approximate sequential pattern mining) that uncovers frequent approximate sequential patterns with various widths. A mined pattern in this framework is representative of a group of sequences that follow the pattern's event flow order. This gives insight into the occurrence of the pattern longitudinally, as well as across the population. The pattern can be recognised as a common pattern across the multiple time series, time, or both.

Mining approximate Time-interval sequential pattern in data stream

Mining approximate Time-interval sequential pattern in data stream

Bitmap-based Algorithm of Mining Approximate Sequential Pattern in Data Stream

Most existing sequential pattern mining algorithms are hard to discover long meaningful sequential patterns in data stream. In this paper, we present BSMDS algorithms, a new approach for mining approximate sequential pattern in data stream, which is based on bitmap and multiple sequence alignment. A new bitmap-based similarity between sequences is defined to cluster similar sequences in the batch. Moreover, a bitmap-based multiple sequence alignment technology is introduced to discover long meaningful sequential patterns. In the initial stage of the algorithm, the data stream is divided into batches. Then, we cluster each batch and make multiple sequence alignment in each cluster to generate the long weighted sequences. Finally, the weighted sequences are filtered with specified threshold to generate the approximate sequential pattern. Our experiments show that BSMDS is very fast and suitable for data stream.

Weighted approximate sequential pattern mining within tolerance factors

In data mining area, weight based sequential pattern mining has been suggested to find important sequential patterns by considering the weights of sequential patterns. More extensions with weight constraints have been proposed such as mining weighted association rules, weighted sequential patterns, weighted closed patterns, frequent patterns with dynamic weights, weighted graphs, weighted sub-trees or sub structures, and so on. In previous approach of weighted frequent sequential pattern mining, weighted supports of sequential patterns are exactly matched to prune weighted infrequent sequential patterns. However, in the noisy environment, the small change in weights or supports of items affects the result sets seriously. This may make the weighted sequential patterns less useful in the noisy environment. In this paper, we propose the robust concept of mining weighted approximate sequential patterns. Based on the framework of weight based sequential pattern mining, an approximate factor is defined to relax the requirement for exact equality between weighted supports of sequential patterns and a minimum threshold. After then, we address the concept of mining weighted approximate sequential frequent patterns to find important sequential patterns with/without the noisy data. We analyze the characteristics of weighted approximate sequential patterns and run extensive performance tests.

Learning Approximate Sequential Patterns for Classification

In this paper, we present an automated approach to discover patterns that can distinguish between sequences belonging to different labeled groups. Our method searches for approximately conserved mo...

Mining sequential patterns from data streams: a centroid approach

In recent years, emerging applications introduced new constraints for data mining methods. These constraints are typical of a new kind of data: the data streams. In data stream processing, memory usage is restricted, new elements are generated continuously and have to be considered in a linear time, no blocking operator can be performed and the data can be examined only once. At this time, only a few methods has been proposed for mining sequential patterns in data streams. We argue that the main reason is the combinatory phenomenon related to sequential pattern mining. In this paper, we propose an algorithm based on sequences alignment for mining approximate sequential patterns in Web usage data streams. To meet the constraint of one scan, a greedy clustering algorithm associated to an alignment method is proposed. We will show that our proposal is able to extract relevant sequences with very low thresholds.

Sequential Pattern Mining in Multi-Databases via Multiple Alignment

To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. Then only the summarized information is forwarded to the global mining process. However, conventional sequential pattern mining methods based on support cannot summarize the local information and is ineffective for global pattern mining from multiple data sources. In this paper, we present an alternative local mining approach for finding sequential patterns in the local databases of a multi-database. We propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. Approximate sequential patterns can effectively summerize and represent the local databases by identifying the underlying trends in the data. We present a novel algorithm, ApproxMAP, to mine approximate sequential patterns, called consensus patterns, from large sequence databases in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. We conduct an extensive and systematic performance study over synthetic and real data. The results demonstrate that ApproxMAP is effective and scalable in mining large sequences databases with long patterns. Hence, ApproxMAP can efficiently summarize a local database and reduce the cost for global mining. Furthremore, we present an elegant and uniform model to identify both high vote sequential patterns and exceptional sequential patterns from the collection of these consensus patterns from each local databases.

대용량 순차 데이터베이스에서 근사 순차패턴 탐색

순차패턴 탐색은 다양한 응용 분야에서 매우 중요한 데이터 마이닝 작업으로 간주된다. 그러나 기존의 순차패턴 탐색 방법들은 길이가 긴 순차패턴이나 노이즈 정보를 다수 포함한 데이터베이스에 대한 마이닝에서는 한계가 있다. 해당 방법들은 매우 짧고 사소한 패턴들은 탐색하지만 다수의 순차 정보들에서 공유되는 중요 패턴들을 분석하는데 어려움을 겪는다. 본 논문에서는 이러한 문제를 해결하기 위한 방법으로 대용량 데이터베이스에 대한 근사 순차패턴 탐색 방법을 제안한다. 근사 순차패턴은 다수의 순차 정보들에서 근사적으로 공유되는 순차패턴을 의미한다. 제안된 방법은 두 과정으로 구분된다. 하나는 유사도에 따라 분석 대상 순차 정보들을 몇 개의 군집으로 나누는 과정이며, 다른 하나는 다중 정렬 방식을 적용하여 각 군집으로부터 대표 패턴을 찾는 과정이다. 이를 위해서 다수의 순차 정보들을 하나로 표현할 수 있는 가중치 순차패턴을 제시하며, 다수의 순차 정보들은 가중치 순차패턴 형태로 통합된다. 이렇게 통합된 정보를 가진 각 가중치 순차패턴을 이용하여 여러 순차 정보와 근사한 하나의 대표 패턴을 생성한다. 끝으로, 다양한 실험을 통해서 제안된 방법의 유용성을 검증한다. Sequential pattern mining is an important data mining task with broad applications. However, conventional methods may meet inherent difficulties in mining databases with long sequences and noise. They may generate a huge number of short and trivial patterns but fail to find interesting patterns shared by many sequences. In this paper, to overcome these problems, we propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. The proposed method works in two steps: one is to cluster target sequences by their similarities and the other is to find consensus patterns that ire similar to the sequences in each cluster directly through multiple alignment. For this purpose, a novel structure called weighted sequence is presented to compress the alignment result, and the longest consensus pattern that represents each cluster is generated from its weighted sequence. Finally, the effectiveness of the proposed method is verified by a set of experiments.