Data Streams Oriented Outlier Detection Method: A Fast Minimal Infrequent Pattern Mining

Outlier detection is a common method for analyzing data streams. In the existing outlier detection methods, most of methods compute distance of points to solve certain specific outlier detection problems. However, these methods are computationally expensive and cannot process data streams quickly. The outlier detection method based on pattern mining resolves the aforementioned issues, but the existing methods are inefficient and cannot meet requirements of quickly mining data streams. In order to improve the efficiency of the method, a new outlier detection method is proposed in this paper. First, a fast minimal infrequent pattern mining method is proposed to mine the minimal infrequent pattern from data streams. Second, an efficient outlier detection algorithm based on minimal infrequent pattern is proposed for detecting the outliers in the data streams by mining minimal infrequent pattern. The algorithm proposed in this paper is demonstrated by real telemetry data of a satellite in orbit. The experimental results show that the proposed method not only can be applied to satellite outlier detection, but also is superior to the existing methods.

Empowering Density-based Micro-clusters In Dynamic Data Stream Clustering

Data stream is a continuous sequence of data generated from various sources and continuously transferred from source to target. Streaming data needs to be processed without having access to all of the data. Some of the sources generating data streams are social networks, geospatial services, weather monitoring, e-commerce purchases, etc. Data stream mining is the process of acquiring knowledge structures from the continuously arriving data. Clustering is an unsupervised machine learning technique that can be used to extract knowledge patterns from the data stream. The mining of streaming data is challenging because the data is in huge amounts and arriving continuously. So the traditional algorithms are not suitable for mining data streams. Data stream mining requires fast processing algorithms using a single scan and a limited amount of memory. The micro clustering has a good role in this. In itself, density based micro clustering has its own unique place in data stream mining. This paper presents a survey on different data clustering algorithms, realizes and empowers the use of density-based micro clusters.

Knowledge Discovery From Evolving Data Streams

Data coming from different sources is referred to as data streams. Data stream mining is an online learning technique where each data point must be processed as the data arrives and discarded as the processing is completed. Progress of technologies has resulted in the monitoring these data streams in real time. Data streams has created many new challenges to the researchers in real time. The main features of this type of data are they are fast flowing, large amounts of data which are continuous and growing in nature, and characteristics of data might change in course of time which is termed as concept drift. This chapter addresses the problems in mining data streams with concept drift. Due to which, isolating the correct literature would be a grueling task for researchers and practitioners. This chapter tries to provide a solution as it would be an amalgamation of all techniques used for data stream mining with concept drift.

Mining Data Streams

In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data. Such data sets which continuously and rapidly grow over time are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging task. Data stream mining is a process of gaining knowledge from continuous and rapid records of data. Due to increased streaming information, data stream mining has attracted the research community in the recent past. There is voluminous of literature which has been published in this domain over the past few years. Due to this, isolating the correct literature would be a grueling task for researchers and practitioners. While addressing a real-world problem, it would be more difficult to find relevant information as it would be hidden in data streams. This chapter tries to provide solution as it would be an amalgamation of all techniques used for data stream mining.