Damped Window Model Research Articles

Advanced incremental erasable pattern mining from the time-sensitive data stream

Pattern mining has been actively advanced and studied in order to process data that is generated in real time, called incremental data. Erasable pattern mining is a concept that extracts erasable patterns as one of the fields belonging to pattern mining. Erasable patterns have useful information in terms of removal, unlike patterns found by other pattern mining methods. The former erasable pattern mining methods, which target incremental data, regard the importance of data identically. However, in real life, recent data are more important and useful than old data, so erasable pattern mining should also process the latest data more significantly in consideration of the feature of real-world data. For this reason, we propose an erasable pattern mining algorithm using an efficient data structure from the time-sensitive data stream. Since the suggested technique performs erasable pattern mining based on a damped window model, it is possible to relatively prioritize the importance of the recent data over the previous data. In addition, it is possible to quickly reflect and efficiently analyze the newly added incremental data due to a list based data structure of the proposed technique. The results of the various performance tests, which are conducted to compare our algorithm with state-of-the-art algorithms, are analyzed, and the analyses indicate that our approach is more efficient than the other latest approaches.

Erasable pattern mining based on tree structures with damped window over data streams

Several pattern mining methods have been proposed to process dynamic data streams because the data generated in industrial fields is continually accumulated. Erasable pattern mining techniques for processing dynamic data streams are needed to discover erasable patterns from dynamic data streams. In previous erasable pattern mining approaches suggested for dynamic data streams, all data are considered to have the same importance regardless of its timestamp. However, dynamic data streams have the characteristic that the new data is relatively more significant than the old data. In erasable pattern mining, one of the desired techniques is an approach in consideration of such characteristic of data streams. For this reason, we propose an erasable pattern mining algorithm over dynamic data streams based on the damped window model. Since the suggested technique considers the new data more important than the previous data, it can find more useful erasable patterns. In addition, erasable pattern mining based on the damped window model is conducted efficiently by employing the tree and table structures. In performance test, we present that our pruning techniques remove unnecessary operations related to invalid erasable patterns efficiently from damped-window-based data streams. Performance evaluation results using real datasets and synthetic datasets show that the proposed approach has good performance with regard to as execution time, pattern generation, and scalability by comparing between the suggested technique and the state of the art algorithms.

Efficient approach of recent high utility stream pattern mining with indexed list structure and pruning strategy considering arrival times of transactions

One of various pattern mining techniques, the High Utility Pattern Mining (HUPM) is a method for finding meaningful patterns from non-binary databases by considering the characteristics of the items. Recently, new data continues to flow over time in diverse fields such as sales data of market, heartbeat sensor data, and social network service. Since these data have a feature that recently generated data have higher influence than the old data, research has been focused on how to efficiently extract hidden knowledge from time-sensitive databases. In this paper, we propose indexed list-based algorithm that mines recent high utility pattern considering the arrival time of inserted data in an environment where new data is continuously accumulated. In other words, to treat the importance of recent data higher than the that of old data, our algorithms reduces the utility values of old transactions according to the time the data is inserted by applying damped window model concept. Moreover, we carry out various experiments to compare our method with state-of-the-art algorithms using real and synthetic datasets in diverse circumstances. Experimental results show that our algorithm outperforms competitors in terms of execution time, memory usage, and scalability test.

Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure

Traditional pattern mining is designed to handle binary database that assume all items in the database have same importance, there is a limitation to recognize accurate information from real-world databases using traditional method. To solve this problem, the high utility pattern mining approaches from non-binary database have been proposed and actively studied by many researchers. Lately, new data is progressively created with the passage of time in diverse area such as biometric data of a patient diagnosed in a medical device and log data of an internet user, and the volume of a database is gradually increasing. A database with these characteristics is called a dynamic database. Under these circumstances, high utility mining techniques suitable for analyzing dynamic databases have recently been extensively studied. In this paper, we propose a new list-based algorithm that mines high utility patterns considering the arrival time of each transaction in an incremental database environment. That is, our algorithm efficiently performs pattern pruning by using a damped window model that considers the importance of the previously inputted data lower than that of recently inserted data and identifies high utility patterns. Experimental results indicate that our proposed method has better performance than the state-of-the-art techniques in terms of runtime, memory, and scalability.

Real time location prediction with taxi-GPS data streams

The prediction of the destination location at the time of pickup is an important problem with potential for substantial impact on the efficiency of a GPS-enabled taxi service. While this problem has been explored earlier in the batch data set-up, we propose in this paper new solutions in the streaming data set-up. We examine four incremental learning methods using a damped window model namely, Multivariate multiple regression, Spherical-spherical regression, Randomized spherical K-NN regression and an Ensemble of these methods for their effectiveness in solving the destination prediction problem. The performance of these methods on several large datasets are evaluated using suitably chosen metrics and they were also compared with some other existing methods. We found that the Multivariate multiple regression method has the best performance in terms of prediction accuracy but the Spherical-spherical regression method is the best performer when we take into account the accuracy time trade-off criterion. The next pickup location problem, where we are interested in predicting the next pickup location for a taxi given the dropoff location coordinates of the previous trip as input is also considered and the aforementioned methods are examined for their suitability using real world datasets. As in the case of destination prediction problem, here also we find that the Multivariate multiple regression method gives better performance than the rest when we consider prediction accuracy but the Spherical-spherical regression method is the best performer when the accuracy-time trade-off criterion is taken into account.

Damped window based high average utility pattern mining over data streams

Data mining methods have been required in both commercial and non-commercial areas. In such circumstances, pattern mining techniques can be used to find meaningful pattern information. Utility pattern mining (UPM) is more suitable for evaluating the usefulness of patterns. The method introduced in this paper employs the high average utility pattern mining (HAUPM) approach, which is one of the UPM approaches and discovers interesting patterns of which the items have more meaningful relations among one another by using a novel utility measure. Meanwhile, past research on pattern mining algorithms mainly focus on mining tasks processing static database such as batch operations. Most continuous, unbounded stream data such as data constantly produced from heart beat sensors should be treated differently with respect to importance because up-to-date data may have higher influence than old data. Therefore, our approach also adopts the concept of the damped window model to gain more useful patterns in stream environments. Various experiments are performed on real datasets in order to demonstrate that the designed method not only provides important, recent pattern information but also requires less computational resources such as execution time, memory usage, scalability and significant test.

A novel approach for data stream clustering using artificial bee colony algorithm

This paper presents a novel approach to effectively clustering a large amount of data stream produced by some applications such as large-scale surveillance, network packet inspection and stock market. Owing to the massiveness and forgotten characteristics of the data stream, the proposed approach uses a damped window model to partition them. Then it adopts modified K-means based on the Artificial Bee Colony (ABC) algorithm to cluster this data stream fragment and dynamically update the clustering result. Detailed simulation analysis demonstrates that this algorithm is of high efficiency of space and time and is more stable.

Particle Swarm Optimized Optimal Threshold Value Selection for Clustering based on Correlation Fractal Dimension

The work on the paper is focused on the use of Fractal Dimension in clustering for evolving data streams. Recently Anuradha et al. proposed a new approach based on Relative Change in Fractal Dimension (RCFD) and damped window model for clustering evolving data streams. Through observations on the aforementioned referred paper, this paper reveals that the formation of quality cluster is heavily predominant on the suitable selection of threshold value. In the above-mentionedpaper Anuradha et al. have used a heuristic approach for fixing the threshold value. Although the outcome of the approach is acceptable, however, the approach is purely based on random selection and has no basis to claim the acceptability in general. In this paper a novel method is proposed to optimally compute threshold value using a population based randomized approach known as particle swarm optimization (PSO). Simulations are done on two huge data sets KDD Cup 1999 data set and the Forest Covertype data set and the results of the cluster quality are compared with the fixed approach. The comparison reveals that the chosen value of threshold by Anuradha et al., is robust and can be used with confidence.