Effective pattern discovery for text mining

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase) based approaches should perform better than the term-based ones, but many experiments did not support this hypothesis. This paper presents an innovative technique, effective pattern discovery which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance

A pattern mining approach for information filtering systems

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well

An Algorithm for Generating Non-Redundant Sequential Rules for Medical Time Series Data

In this paper, an algorithm for generating non-redundant sequential rules for the medical time series data is designed. This study is the continuation of my previous study titled �An Algorithm for Mining Closed Weighted Sequential Patterns with Flexing Time Interval for Medical Time Series Data� [25]. In my previous work, the sequence weight for each sequence was calculated based on the time interval between the itemsets.Subsequently, the candidate sequences were generated with flexible time intervals initially. The next step was, computation of frequent sequential patterns with the aid of proposed support measure. Next the frequent sequential patterns were subjected to closure checking process which leads to filter the closed sequential patterns with flexible time intervals. Finally, the methodology produced with necessary sequential patterns was proved. This methodology constructed closed sequential patterns which was 23.2% lesser than the sequential patterns. In this study, the sequential rules are generated based on the calculation of confidence value of the rule from the closed sequential pattern. Once the closed sequential rules are generated which are subjected to non-redundant checking process, that leads to produce the final set of non-redundant weighted closed sequential rules with flexible time intervals. This study produces non-redundant sequential rules which is 172.37% lesser than sequential rules

Efficient Mining of Sequential Patterns in a Sequence Database with Weight Constraint

Sequence pattern mining is one of the essential data mining tasks with broad applications. Many sequence mining algorithms have been developed to find a set of frequent sub-sequences satisfying the support threshold in a sequence database. The main problem in most of these algorithms is they generate huge number of sequential patterns when the support threshold is low and all the sequence patterns are treated uniformly while real sequential patterns have different importance. In this paper, we propose an algorithm which aims to find more interesting sequential patterns, considering the different significance of each data element in a sequence database. Unlike the conventional weighted sequential pattern mining, where the weights of items are preassigned according to the priority or importance, in our approach the weights are set according to the real data and during the mining process not only the supports but also weights of patterns are considered. The experimental results show that the algorithm is efficient and effective in generating more interesting patterns