Chameleon Clustering Research Articles

Clustering-based fusion for medical information retrieval

Medicine is a fast-moving field, and the number of medical publications has increased rapidly over recent years. How to find relevant information from this vast pool of research effectively and efficiently has therefore become highly challenges. Previous studies have demonstrated that data fusion can improve search performance if properly utilized. However, in most cases effectiveness is the only concern and efficiency is not considered. A fusion-based system is by nature more complicated and expensive computationally than other retrieval models such as BM25, because many component retrieval systems and an extra layer of fusion are required. The number of component retrieval systems involved is an important indicator of complexity of the fusion-based system. We aim to select the optimal k-subset of component retrieval systems for any given number k, to optimize both fusion performance and reduce the cost of data fusion. A clustering-based approach is proposed. First all the candidates are divided into clusters by the Chameleon clustering algorithm, then representatives from every cluster are chosen by Sequential Forward Selection for fusion. Evaluated with two datasets from TREC, the proposed method performs more effectively than the other baseline methods including the state-of-the-art subset selection method significantly. When either of the two typical fusion methods is used, an improvement rate of over 10% is observed for both measures Mean Average Precision and Recall-level Precision, and an improvement rate of over 5% is observed for both measures Precision at 10 document level and Mean Reciprocal Rank.

A human body physiological feature selection algorithm based on filtering and improved clustering.

ResearchThe body composition model is closely related to the physiological characteristics of the human body. At the same time there can be a large number of physiological characteristics, many of which may be redundant or irrelevant. In existing human physiological feature selection algorithms, it is difficult to overcome the impact that redundancy and irrelevancy may have on human body composition modeling. This suggests a role for selection algorithms, where human physiological characteristics are identified using a combination of filtering and improved clustering. To do this, a feature filtering method based on Hilbert-Schmidt dependency criteria is first of all used to eliminate irrelevant features. After this, it is possible to use improved Chameleon clustering to increase the combination of sub-clusters amongst the characteristics, thereby removing any redundant features to obtain a candidate feature set for human body composition modeling. MethodWe report here on the use of an algorithm to filter the characteristic parameters in INBODY770 (this paper used INBODY 770 as body composition analyzer.) measurement data, which has three commonly-used impedance bands (1 kHZ, 250 kHZ, 500 kHZ). This algorithm is able to filter out parameters that have a low correlation with body composition BFM. The algorithm is also able to draw upon improved clustering techniques to reduce the initial feature set from 29 parameters to 10 parameters for any parameters of the 250 kHZ band that remain after filtering. In addition, we also examined the impact of different sample sizes on feature selection.ResultThe proposed algorithm is able to remove irrelevant and redundant features and the resulting correlation between the model and the body composition (BFM which is a whole body fat evaluation can better assess the body's overall fat and muscle composition.) is 0.978, thereby providing an improved model for prediction with a relative error of less than 0.12.

Topology and Topic-Aware Service Clustering

This article describes how the number of services and their types being so numerous makes accurately discovering desired services become a problem. Service clustering is an effective way to facilitate service discovery. However, the existing approaches are usually designed for a single type of service documents, neglecting to fully use the topic and topological information in service profiles and usage histories. To avoid these limitations, this article presents a novel service clustering approach. It adopts a bipartite network to describe the topological structure of service usage histories and uses a SimRank algorithm to measure the topological similarity of services; It applies Latent Dirichlet Allocation to extract topics from service profiles and further quantifies the topic similarity of services; It quantifies the similarity of services by integrating topological and topic similarities; It uses the Chameleon clustering algorithm to cluster the services. The empirical evaluation on real-world data set highlights the benefits provided by the combination of topological and topic similarities.

基于耦合关系的医生用药异常分析

近些年来，医患关系受到广泛关注。如何准确地挖掘异常用药是制约医生和缓减医患关系的关键。本文提出了一种检测医生用药异常的总体框架。该框架集成处方数据的耦合相似度度量和变色龙聚类算法，并分为三个阶段，定性分析，定量分析和异常检测。在真实处方数据上进行了充分的实验，实验验证了该框架能够有效地检测出医生用药异常。 In recent years, the doctor-patient relationship has received extensive attention. How to exactly mine the abnormal use of drugs is the vital to constrain doctors and relieve the doctor-patient relationship. This paper presents a general framework for detecting the abnormal use of drugs. The framework integrates coupled similarity and Chameleon clustering algorithm, and consists of three stages, qualitative analysis, quantitative analysis and abnormal detection. We conduct extensive experiments on real-world prescription data. The experiments evaluate that the framework can efficiently detect abnormal use of drugs.

Chameleon Clustering Algorithm with Semantic Analysis Algorithm for Efficient Web Usage Mining

World Wide Web (WWW) has increasing information, and the websites usage also increasing rapidly. The web server records the users’ search activity on the web and store it in the server log. When these web logs properly mined, lot of useful information can be provided for the decision making. Thus the process of accessing relevant information becomes easier. The web log mining usually used for the purpose of generating a model for search pattern discovery with the semantic meaning. Hence the CHAMELEON clustering algorithm is used for the clustering the user search logs based on the search category. Semantic analysis is performed in addition on the clustered logs to extract the semantically similar patterns. This process of discovering the frequent search pattern based on semantic relation can be used for providing user pertinent information using web usage mining. Therefore, the proposed system brings the frequent patterns as output with semantical information that supports the user to understand the pattern semantically.

Parallel Chameleon Clustering Based on MapReduce

With the enlarging volumes of datasets in various areas and the rapid development of distributed technologies, parallel clustering is becoming increasingly important. To cluster large-scale data of various shapes, this paper proposes a parallel Chameleon clustering algorithm. The key idea is using a parallel minimum spanning tree algorithm to generate the initial clusters after obtaining the k-nearest neighbor graph of the original dataset in a parallel way inspired by matrix multiplication, and then using strategies suggested by the primary Chameleon clustering to combine clusters and obtain the final clusters. Finally, we design the parallel Chameleon clustering based on MapReduce. Experiments show that this algorithm is efficient and well-performed.

Parallel Algorithm for the Chameleon Clustering Algorithm using Dynamic Modeling

With the increasing size of data-sets in application areas like bio-medical, hospitals, information systems, scientific data processing and predictions, finance analytics, communications, retail and marketing, it is becoming increasingly important to execute data mining tasks in parallel. At the same time, technological advancements have made shared memoryparallel computation machines commonly available to various organizations and individuals. This paper analyzes a hierarchical clustering algorithm named chameleon clustering which is based on dynamic modeling and we propose a parallel algorithm for the same. The algorithm utilizes the concept of parallel processors available and hence reduces the time to generate final clusters. General Terms Enhancement, Utilization, clustering, similarity, Algorithms et. al.