PHD: an efficient data clustering scheme using partition space technique for knowledge discovery in large databases

Abstract

Rapid technological advances imply that the amount of data stored in databases is rising very fast. However, data mining can discover helpful implicit information in large databases. How to detect the implicit and useful information with lower time cost, high correctness, high noise filtering rate and fit for large databases is of priority concern in data mining, specifying why considerable clustering schemes have been proposed in recent decades. This investigation presents a new data clustering approach called PHD, which is an enhanced version of KIDBSCAN. PHD is a hybrid density-based algorithm, which partitions the data set by K-means, and then clusters the resulting partitions with IDBSCAN. Finally, the closest pairs of clusters are merged until the natural number of clusters of data set is reached. Experimental results reveal that the proposed algorithm can perform the entire clustering, and efficiently reduce the run-time cost. They also indicate that the proposed new clustering algorithm conducts better than several existing well-known schemes such as the K-means, DBSCAN, IDBSCAN and KIDBSCAN algorithms. Consequently, the proposed PHD algorithm is efficient and effective for data clustering in large databases.

Statistical user model supported by R-Tree structure

“…In this line, there are several works in the bibliography applicable to the partition formulation design. For example, most clustering solutions like K-means [49] or C-means algorithms [7,48] can be adapted to explore alternatives and eventually enhance the proposed solution for the partition process in specific domains. There also can be found solutions suited to high-dimensionality data [36].…”

Towards combinatorial clustering: preliminary research survey

“…Many applications based on very large data sets/networks require fast clustering approaches (e.g., [127,457,546,585,600,662]). In Table 3 In Table 3.35, a list of basic fast local clustering algorithms (i.e., fast sub-algorithms) is presented.…”

Zoning by k-Means over a Large Data Set

“…Zoning process is very important to simplify logistic, commercialization, maintenance or emergency decisions (Tsai et al, 2010). When the number of cities is very large, zoning is a previous step for the design of routing strategies (TSP, VRP), as well as for the grounds for the process of making decisions about location issues.…”