Mining frequent biological sequences based on bitmap without candidate sequence generation

Biological sequences carry a lot of important genetic information of organisms. Furthermore, there is an inheritance law related to protein function and structure which is useful for applications such as disease prediction. Frequent sequence mining is a core technique for association rule discovery, but existing algorithms suffer from low efficiency or poor error rate because biological sequences differ from general sequences with more characteristics. In this paper, an algorithm for mining Frequent Biological Sequence based on Bitmap, FBSB, is proposed. FBSB uses bitmaps as the simple data structure and transforms each row into a quicksort list QS-list for sequence growth. For the continuity and accuracy requirement of biological sequence mining, tested sequences used during the mining process of FBSB are real ones instead of generated candidates, and all the frequent sequences can be mined without any errors. Comparing with other algorithms, the experimental results show that FBSB can achieve a better performance on both run time and scalability

OLAP-Sequential Mining: Summarizing Trends from Historical Multidimensional Data using Closed Multidimensional Sequential Patterns

International audienceData warehouses are now well recognized as the way to store historical data that will then be available for future queries and analysis. In this context, some challenges are still open, among which the problem of mining such data. OLAP mining, introduced by J. Han in 1997, aims at coupling data mining techniques and data warehousing. These techniques have to take the specificities of such data into account. One of the specificities that is often not addressed by classical methods for data mining is the fact that data warehouses describe data through several dimensions. Moreover, the data are stored through time, and we thus argue that sequential patterns are one of the best ways to summarize the trends from such databases. Sequential pattern mining aims at discovering correlations among events through time. However, the number of patterns can become very important when taking several analysis dimensions into account, as it is the case in the framework of multidimensional databases. This is why we propose here to define a condensed representation without loss of information: closed multidimensional sequential patterns. This representation introduces properties that allow to deeply prune the search space. In this paper, we also define algorithms that do not require candidate set maintenance. Experiments on synthetic and real data are reported and emphasize the interest of our proposal

Implementation of an interactive pattern mining framework on electronic health record datasets

Large collections of electronic patient records contain a broad range of clinical information highly relevant for data analysis. However, they are maintained primarily for patient administration, and automated methods are required to extract valuable knowledge for predictive, preventive, personalized and participatory medicine. Sequential pattern mining is a fundamental task in data mining which can be used to find statistically relevant, non-trivial temporal dependencies of events such as disease comorbidities. This works objective is to use this mining technique to identify disease associations based on ICD-9-CM codes data of the entire Taiwanese population obtained from Taiwan’s National Health Insurance Research Database. This thesis reports the development and implementation of the Disease Pattern Miner – a pattern mining framework in a medical domain. The framework was designed as a Web application which can be used to run several state-of-the-art sequence mining algorithms on electronic health records, collect and filter the results to reduce the number of patterns to a meaningful size, and visualize the disease associations as an interactive model in a specific population group. This may be crucial to discover new disease associations and offer novel insights to explain disease pathogenesis. A structured evaluation of the data and models are required before medical data-scientist may use this application as a tool for further research to get a better understanding of disease comorbidities

Frequent Pattern mining with closeness Considerations: Current State of the art

Due to rising importance in frequent pattern mining in the field of data mining research, tremendous progress has been observed in fields ranging from frequent itemset mining in transaction databases to numerous research frontiers. An elaborative note on current condition in frequent pattern mining and potential research directions is discussed in this article. It2019;s a strong belief that with considerably increasing research in frequent pattern mining in data analysis, it will provide a strong foundation for data mining methodologies and its applications which might prove a milestone in data mining applications in mere future

Mining frequent sequential patterns in data streams using SSM-algorithm.

Frequent sequential mining is the process of discovering frequent sequential patterns in data sequences as found in applications like web log access sequences. In data stream applications, data arrive at high speed rates in a continuous flow. Data stream mining is an online process different from traditional mining. Traditional mining algorithms work on an entire static dataset in order to obtain results while data stream mining algorithms work with continuously arriving data streams. With rapid change in technology, there are many applications that take data as continuous streams. Examples include stock tickers, network traffic measurements, click stream data, data feeds from sensor networks, and telecom call records. Mining frequent sequential patterns on data stream applications contend with many challenges such as limited memory for unlimited data, inability of algorithms to scan infinitely flowing original dataset more than once and to deliver current and accurate result on demand. This thesis proposes SSM-Algorithm (sequential stream mining-algorithm) that delivers frequent sequential patterns in data streams. The concept of this work came from FP-Stream algorithm that delivers time sensitive frequent patterns. Proposed SSM-Algorithm outperforms FP-Stream algorithm by the use of a hash based and two efficient tree based data structures. All incoming streams are handled dynamically to improve memory usage. SSM-Algorithm maintains frequent sequences incrementally and delivers most current result on demand. The introduced algorithm can be deployed to analyze e-commerce data where the primary source of the data is click stream data. (Abstract shortened by UMI.)Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .M668. Source: Masters Abstracts International, Volume: 44-03, page: 1409. Thesis (M.Sc.)--University of Windsor (Canada), 2005

OSSM: Ordered Sequence set mining for maximal length frequent sequences

The process of finding sequential rules is an indispensable in frequent sequence mining. Generally, in sequence mining algorithms, suitable methodologies like a bottom2013;up approach will be used for creating large sequences from tiny patterns. This paper proposed on an algorithm that uses a hybrid two-way (bottom-up and top-down) approach for mining maximal length sequences. The model proposed is opting to bottom-up approach called 201C;Concurrent Edge Prevision and Rear Edge Pruning (CE