The discovery of sequential patterns, which extends beyond frequent item-set finding of association rule mining, has become a challenging task due to its complexity. Essentially, a user would specify a minimum support threshold with respect to the database to find out the desired patterns. The mining process is usually iterative since the user must try various thresholds to obtain the satisfact...

Data mining is an iterative process. Users issue series of similar data mining queries, in each consecutive run slightly modifying either the definition of the mined dataset, or the parameters of the mining algorithm. This model of processing is most suitable for incremental mining algorithms that reuse the results of previous queries when answering a given query. Incremental mining algorithms ...

Clustering is one of the important methods in data mining to weight the distance between the cluster objects effectively. Existing K-means algorithm with Affinity Propagation (AP) algorithm captures the structural information of texts, and improve the semi supervised clustering process. Seeds Affinity Propagation (SAP) provides the detailed distance measurement but it takes the longer execution...

We propose an iterative spatial-temporal mining algorithm for identifying and extracting events from social media. One of the key aspects of the proposed algorithm is a signal processing-inspired approach for viewing spatial-temporal term occurrences as signals, analyzing the noise contained in the signals, and applying noise filters to improve the quality of event extraction from these signals...

There has been increased interest in time series data mining recently. In some cases, approaches of real-time segmenting time series are necessary in time series similarity search and data mining, and this is the focus of this paper. A real-time iterative algorithm that is based on time series prediction is proposed in this paper. Proposed algorithm consists of three modular steps. (1) Modeling...

in this paper, we introduce and study a mixed variational inclusion problem involving infinite family of fuzzy mappings. an iterative algorithm is constructed for solving a mixed variational inclusion problem involving infinite family of fuzzy mappings and the convergence of iterative sequences generated by the proposed algorithm is proved. some illustrative examples are also given.

Mining generalized association rules among items in the presence of taxonomy and with nonuniform minimum support has been recognized as an important model in the data mining community. In real applications, however, the work of discovering interesting association rules is an iterative process; the analysts have to continuously adjust the constraint of minimum support to discover real informativ...