With the limited memory and time, a fast and effective clustering can’t be achieved for massive, highspeed data stream, so this paper mainly studies the key method of data stream clustering under the restriction of resource, and then proposes a dynamic data stream clustering algorithm (D-DStream) based on wavelet network and density, which uses sliding window to process data stream. Firstly, ap...

The data stream model has recently attracted attention for its applicability to numerous types of data, including telephone records, Web documents, and click streams. For analysis of such data, the ability to process the data in a single pass, or a small number of passes, while using little memory, is crucial. D-Stream algorithm is an extended grid-based clustering algorithm for different dimen...

Nowadays with the rapid development of wireless sensor networks, and network traffic monitoring, stream data gradually becomes one of the most popular data models. Stream data is different from the traditional static data. Clustering analysis is an important technology of data mining, so that many researchers pay their attention to the clustering of stream data. In this paper, MSFS(Multiple Spe...

Data stream mining has attracted considerable attention over the past few years owing to the significance of its applications. Streaming data is often evolving over time. Capturing changes could be used for detecting an event or a phenomenon in various applications. Weather conditions, economical changes, astronomical, and scientific phenomena are among a wide range of applications. Because of ...

We propose an efficient Frequent Sequence Stream algorithm for identifying the top k most frequent subsequences over big data streams. Our Sequence Stream algorithm gains its efficiency by its time complexity of linear time and very limited space complexity. With a pre-specified subsequence window size S and the k value, in very high probabilities, the Sequence Stream algorithm retrieve the top...

Mining frequent itemsets through static Databases has been extensively studied and used and is always considered a highly challenging task. For this reason it is interesting to extend it to data streams field. In the streaming case, the frequent patterns’ mining has much more information to track and much greater complexity to manage. Infrequent items can become frequent later on and hence cann...

The present paper proposes a new algorithm for discovering similar sections between two time sequence data sets. The algorithm, called Partial Matching Discovery, or PMD, is based on Dynamic Programming. PMD realizes fast matching between arbitrary sections in reference stream data and input stream data and enables the extraction of similar sections in a synchronous manner with the input data. ...

Mining patterns that compress the data well was shown to be an effective approach for extracting meaningful patterns and solving the redundancy issue in frequent pattern mining. Most of the existing works in the literature consider mining compressing patterns from a static database of itemsets or sequences. These approaches require multiple passes through the data and do not scale up with the s...