Todays, in many modern applications, we search for frequent and repeating patterns in the analyzed data sets. In this search, we look for patterns that frequently appear in data set and mark them as frequent patterns to enable users to make decisions based on these discoveries. Most algorithms presented in the context of data stream mining and frequent pattern detection, work either on uncertai...

Clustering streams of continuously arriving data has become an important application of data mining in recent years and efficient algorithms have been proposed by several researchers. However, clustering alone neglects the fact that data in a data stream is not only characterized by the proximity of data points which is used by clustering, but also by a temporal component. The extensible Markov...

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. The continuous characteristic of streaming data necessitates the use of algorithms that require only one scan over the stream for knowledge discovery. Data mining over data streams should support the flexible trade-off between processing time and mining accuracy. In many application areas, min...

We investigate the problem of finding the frequent items in a continuous data stream. We present an algorithm called λ-Count for computing frequency counts over a user specified threshold on a data stream. To emphasize the importance of the more recent data items, a fading factor  is used. Our algorithm can detect εapproximate frequent items of a data stream using O(logλε) memory space and O(1...

Efficient one-pass estimation of F0, the number of distinct elements in a data stream, is a fundamental problem arising in various contexts in databases and networking. We consider range-efficient estimation of F0: estimation of the number of distinct elements in a data stream where each element of the stream is not just a single integer, but an interval of integers. We present a randomized alg...

  In this paper, modeling and optimization of Fischer-Tropsch Synthesis is considered in a fixed-bed catalytic reactor using an industrial Fe-Cu-K catalyst. A one dimensional pseudo-homogenous plug flow model without axial dispersion is developed for converting syngas to heavy hydrocarbons. The effects of temperature, pressure, H2 to CO ratio in feed stream, and CO molar flow on the mass flow r...

We prove Ω(n) deterministic lower bounds for any streaming algorithm that exactly computes the number of inversions (2-dec-count) in a data stream of t elements where each element comes from [n] and t ≥ Ω(n). The proof uses a reduction argument and utilizes communication lower bounds for computation of disjointness. Our second result is Ω(n) lower bound on any algorithm that correctly computes ...

To solve the clustering algorithm based on grid density on uncertain data stream in adjustment cycle for clustering omissions, the paper proposed an algorithm, named GCUDS, to cluster uncertain data steam using grid structure. The concept of the data trend degree was defined to describe the grade of a data point belonging to some grid unit and the defect of information loss around grid units wa...

Conventional stream mining algorithms focus on single and stand-alone mining tasks. Given the single-pass nature of data streams, it makes sense to maximize throughput by performing multiple complementary mining tasks concurrently. We investigate the potential of concurrent semi-supervised learning on data streams and propose an incremental algorithm called CSL-Stream (Concurrent Semi–supervise...