Certain Investigation on Dynamic Clustering in Dynamic Datamining (original) (raw)

A Density Based Dynamic Data Clustering Algorithm based on Incremental Dataset

Journal of Computer Science, 2012

Problem statement: Clustering and visualizing high-dimensional dynamic data is a challenging problem. Most of the existing clustering algorithms are based on the static statistical relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic data clustering algorithms. Approach: In this work, we present a density based dynamic data clustering algorithm for clustering incremental dataset and compare its performance with full run of normal DBSCAN, Chameleon on the dynamic dataset. Most of the clustering algorithms perform well and will give ideal performance with good accuracy measured with clustering accuracy, which is calculated using the original class labels and the calculated class labels. However, if we measure the performance with a cluster validation metric, then it will give another kind of result. Results: This study addresses the problems of clustering a dynamic dataset in which the data set is increasing in size over time by adding more and more data. So to evaluate the performance of the algorithms, we used Generalized Dunn Index (GDI), Davies-Bouldin index (DB) as the cluster validation metric and as well as time taken for clustering. Conclusion: In this study, we have successfully implemented and evaluated the proposed density based dynamic clustering algorithm. The performance of the algorithm was compared with Chameleon and DBSCAN clustering algorithms. The proposed algorithm performed significantly well in terms of clustering accuracy as well as speed.

Fast And Enhanced Algorithms For Dynamic Dataset

Now-a-days, data is growing at a phenomenal rate from terabytes to petabytes. This growth is reflected in an increase in both the size and complexity as individual data base as well as in a proliferation of new databases. In practical applications, data are usually presented to the users in a modified form, tailored to satisfy specific needs. Despite this, people must analyze data more or less manually, acting as sophisticated query processors. Finding a pattern or grouping the data is carried out by clustering techniques. In this paper two new algorithms for clustering of dynamic data, the first algorithm is FAST K-Means Clustering Algorithm for Dynamic Mining (F KCADM) and another one is Enhanced K-Means Algorithm for Dynamic Mining (EKMDM) is discussed and proposed.

A methodology for dynamic data mining based on fuzzy clustering

Fuzzy sets and systems, 2005

Dynamic data mining is increasingly attracting attention from the respective research community. On the other hand, users of installed data mining systems are also interested in the related techniques and will be even more since most of these installations will need to be updated in the future. For each data mining technique used, we need di erent methodologies for dynamic data mining. In this paper, we present a methodology for dynamic data mining based on fuzzy clustering. Using the implementation of the proposed system we show its beneÿts in two application areas: customer segmentation and tra c management. c

Dynamic Clustering of Data with Modified K-Means Algorithm

K-means is a widely used partitional clustering method. While there are considerable research efforts to characterize the key features of K-means clustering, further investigation is needed to reveal whether the optimal number of clusters can be found on the run based on the cluster quality measure. This paper presents a modified K-means algorithm with the intension of improving cluster quality and to fix the optimal number of cluster. The K-means algorithm takes number of clusters (K) as input from the user. But in the practical scenario, it is very difficult to fix the number of clusters in advance. The proposed method works for both the cases i.e. for known number of clusters in advance as well as unknown number of clusters. The user has the flexibility either to fix the number of clusters or input the minimum number of clusters required. In the former case it works same as K-means algorithm. In the latter case the algorithm computes the new cluster centers by incrementing the cl...

A Method for Dynamic Clustering of Data

1998

This paper describes a method for the segmentation of dynamic data. It extends well known algorithms developed in the context of static clustering (e.g., the c-means algorithm, Kohonen maps, elastic nets and fuzzy c-means). The work is based on an unified framework for constrained clustering recently proposed by the authors in [1]. This framework is extended by using a motion model for the clusters which includes global and local evolution of the data centroids. A noise model is also proposed to increase the robustness of the dynamic clustering algorithm with respect to outliers.

Dynamic Pattern Mining: An Incremental Data Clustering Approach

Lecture Notes in Computer Science, 2005

We propose a mining framework that supports the identification of useful patterns based on incremental data clustering. Given the popularity of Web news services, we focus our attention on news streams mining. News articles are retrieved from Web news services, and processed by data mining tools to produce useful higher-level knowledge, which is stored in a content description database. Instead of interacting with a Web news service directly, by exploiting the knowledge in the database, an information delivery agent can present an answer in response to a user request. A key challenging issue within news repository management is the high rate of document insertion. To address this problem, we present a sophisticated incremental hierarchical document clustering algorithm using a neighborhood search. The novelty of the proposed algorithm is the ability to identify meaningful patterns (e.g., news events, and news topics) while reducing the amount of computations by maintaining cluster structure incrementally. In addition, to overcome the lack of topical relations in conceptual ontologies, we propose a topic ontology learning framework that utilizes the obtained document hierarchy. Experimental results demonstrate that the proposed clustering algorithm produces high-quality clusters, and a topic ontology provides interpretations of news topics at different levels of abstraction.

Incremental clustering for dynamic information processing

ACM Transactions on Information Systems, 1993

Clustering of very large document databases is useful for both searching and browsing. The periodic updating of clusters is required due to the dynamic nature of databases. An algorithm for incremental clustering is introduced. The complexity and cost analysis of the algorithm together with an investigation of its expected behavior are presented. Through empirical testing it is shown that the algorithm achieves cost effectiveness and generates statistically valid clusters that are compatible with those of reclustering. The experimental evidence shows that the algorithm creates an effective and efficient retrieval environment.

Incremental Shared Nearest Neighbor Density-Based Clustering Algorithms for Dynamic Datasets

2016

Dynamic datasets undergo frequent changes where small number of data points are added and deleted. Such dynamic datasets are frequently encountered in many real world applications such as search engines and recommender systems. Incremental data mining algorithms process these updates to datasets efficiently to avoid redundant computation. Shared nearest neighbor density based clustering (SNN-DBSCAN) is a widely used clustering algorithm, mainly for its robustness. Existing incremental extension to SNNDBSCAN cannot handle deletions to dataset and handles insertions only point by point. We overcome both these bottlenecks by efficiently identifying affected parts of clusters while processing updates to dataset in batch mode. We present three different incremental algorithms with varying efficiency at elimination of redundant computation. We show effectiveness of our algorithms by performing experiments on large synthetic as well as real world datasets. Our algorithms are up to 2 Orders...

Batch Incremental Shared Nearest Neighbor Density Based Clustering Algorithm for Dynamic Datasets

Lecture Notes in Computer Science, 2017

Incremental data mining algorithms process frequent updates to dynamic datasets efficiently by avoiding redundant computation. Existing incremental extension to shared nearest neighbor density based clustering (SNND) algorithm cannot handle deletions to dataset and handles insertions only one point at a time. We present an incremental algorithm to overcome both these bottlenecks by efficiently identifying affected parts of clusters while processing updates to dataset in batch mode. We show effectiveness of our algorithm by performing experiments on large synthetic as well as real world datasets. Our algorithm is up to four orders of magnitude faster than SNND and requires up to 60% extra memory than SNND while providing output identical to SNND.

View project Clustering in Dynamic Data , Detection Concept Change in Dynamic Data View project

2017

Unsupervised machine learning approa ches involving several clustering algorithms working together to tackle difficult data sets are a recent area of research with a large number of applications such as clustering of distributed data, multi-expert clustering, multi-scale clustering analysis or multi-view clustering. Most of these frameworks can be regrouped under the umbrella of collaborative clustering, the aim of which is to reveal the common underlying structures found by the different algorithms while analyzing the data. Within this context, the purpose of this article is to propose a collaborative framework lifting the limitations of many of the previously proposed methods: Our proposed collaborative learning method makes possible for a wide range of clustering algorithms from different families to work together based solely on their clustering solutions, thus lifting previous limitation requiring identical prototypes between the different collaborators. Our proposed framework ...