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In the Summer 1983 issue of this journal, Heany and Weiss introduced “cluster strategy” into the lexicon of strategic planning. They argued that in diversified corporations, additional leverage can be gained by integrating the strategies of a number of related strategic business units (SBUs), thereby giving rise to a cluster strategy. They proposed seven bases for formulating clusters (quest for efficiency, market relatedness, shared production processes, underlying technology, market systems relationships, distribution system, company specific) and also touched upon an administrative problem involved in establishing business clusters. That is, only CEOs have sufficient clout to translate perceived strategic relationships among SBUs into a unified strategy based on inter‐SBU cooperation.

The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm.

This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm.

Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments.

The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents.

This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.

As interest in clusters has increased in recent decades, so has related research. To date, studies have explored the advantages of location within a cluster from an economic perspective, and much print has been devoted to describing clusters. Still to be addressed, however, is the question of whether economic advantage varies by cluster type and whether the competitive advantages that accrue to cluster firms vary according to both the location of the clusters and their concentration. Similarly, no empirical research has yet been carried out that specifically addresses the impact of clusters on entrepreneurial activity, in an effort to determine whether they enhance such activity. This paper begins by reviewing they key literature concerning cluster development and entrepreneurship. Hypotheses regarding the intensity of entrepreneurial activity in relation to cluster location and concentration are then presented and evaluated. The findings suggest that concentrations of multiple clusters do serve to enhance entrepreneurial activity and that, consequently, certain regions are naturally advantaged by the presence of these concentrations. A discussion follows, in which the implications of these findings for entrepreneurship and for regional policymakers are considered. Finally, directions for future research are proposed.

The micro‐satellite clusters have been discussed for several years, however, there is not a common framework about its software, and various researches distributed at different domains. In order to conduct the future work well, the purpose of this paper is to systematically describe micro‐satellite clusters' characteristics, clusters software model, and present a distributed testbed to shorten test process, and minimize the development cost.

The cluster characteristics and model is summarized through analyzing the past satellite cluster programs. Then the ground test system is designed to shorten micro‐satellite's development period, improve its reliability.

The clusters' characteristics are discussed, such as coverage, scalability, fault tolerance, low cost, etc. The clusters' data flow and on‐board software architecture are presented according to properties of clusters. Finally, the distributed testbed that focuses on future on‐board software and hardware technologies that aim to rapid design, build, integration, test, deployment, and operation of the future micro‐satellite is designed.

The presentation of software architecture of cluster member can improve the micro‐satellite's development, and the distributed testbed can improve the ground test efficiency, especially, when the micro‐satellite quantity is big.

A wide number of applications requires classifying or grouping data into a set of categories or clusters. The most popular clustering techniques to achieve this objective are K‐means clustering and hierarchical clustering. However, both of these methods necessitate the a priori setting of the cluster number. The purpose of this paper is to present a clustering method based on the use of a niching genetic algorithm to overcome this problem.

The proposed approach aims at finding the best compromise between the inter‐cluster distance maximization and the intra‐cluster distance minimization through the silhouette index optimization. It is capable of investigating in parallel multiple cluster configurations without requiring any assumption about the cluster number.

The effectiveness of the proposed approach is demonstrated on 2D benchmarks with non‐overlapping and overlapping clusters.

The proposed approach is also applied to the clustering analysis of railway driving profiles in the context of hybrid supply design. Such a method can help designers to identify different system configurations in compliance with the corresponding clusters: it may guide suppliers towards “market segmentation”, not only fulfilling economic constraints but also technical design objectives.

The purpose of this paper is to attempt to calculate the trust degree between two enterprises in an industrial network using grey correlation degree algorithm for exploring characteristics of community structure and evolution rules of cluster cooperation networks in axle‐type and satellite‐type clusters.

Starting from analysis of trust formation mechanism of inter‐enterprise in industrial networks, adjacency of inter‐enterprise relationship, their information acquisition ability, their influence power in network and their past interaction experience are chosen as influencing factors of the trust between two enterprises. Grey correlation degree algorithm was chosen to calculate the trust degree between two enterprises in an industrial network. According to the rules of dynamic adjustment of trust degree originated from thoughts of the prisoners' dilemma model, computer simulation is applied to explore characteristics of community structure and evolution rules of cluster cooperation network in axle‐type and satellite‐type clusters.

With the dynamic adjustment of enterprises' trust degree, the network density of axle‐type and satellite‐type cluster networks was decreasing as the cluster scale was enlarging, and eventually tended to be stable; community structure was emerged in axle‐type and satellite‐type industrial clusters as the cluster scale was enlarging; community characteristics were obviously stronger in axle‐type cluster networks than in satellite‐type; communities were overlapped in axle‐type cluster networks, that is, bridge nodes emerged between communities.

This paper is the first to apply the grey correlation degree algorithm to calculate the trust degree between two enterprises in cluster networks for designing the rules of dynamic adjustment of trust degree.

The purpose of this paper is to examine clustering search results. Traditionally, search results from professional online information services presented the results in reverse chronological order. Later, relevance ranking was introduced for ordering the display of the hits on the result list to separate the wheat from the chaff.

The need for better presentation of search results retrieved from millions, then billions, of highly unstructured and untagged Web pages became obvious. Clustering became a popular software tool to enhance relevance ranking by grouping items in the typically very large result list. The clusters of items with common semantic and/or other characteristics can guide the users in refining their original queries, to zoom in on smaller clusters and drill down through sub‐groups within the cluster.

Despite its proven efficiency, clustering is not available, except for Ask, in the primary Web‐wide search engines (Windows Live, Yahoo and Google).

Smaller, secondary Web‐wide search engines (WiseNut, Gigablast, and especially Exalead) offer good clustering options.

This paper aims to critically examine the care clusters descriptors now being introduced in mental health care in England and to discuss them in the context of trying to further approaches to co‐production (and related concepts), and social inclusion and recovery. The paper seeks to introduce a revised set of cluster descriptors that are more lay friendly and that, hence, would be likely to encourage more service user engagement in care.

The care cluster descriptors are critically examined within the context of desires to engage service users in care and encourage staff to explicitly consider individual strengths as well as needs, i.e. co‐production of care between active service users and providers.

The implementation of care clusters and the development of new organisations of care based on these present opportunities to further develop in progressive ways how care is planned and developed. The cluster descriptors, however, are not an ideal basis for this. Being designed for one purpose the descriptors do not encourage thinking about individual strengths nor are they very lay friendly. They are not seen as an ideal basis for more actively engaging individuals in the planning and organisation of their care packages. Hence, revised descriptors felt to be more suited to this are presented.

Furthering more recovery oriented and socially inclusive practice in mental health care requires that each part of the system encourages all individuals involved to think in these ways. As the starting point for thinking about care, it is essential that cluster descriptors also work in this way. Services need to consider how the existing cluster descriptors impact on how individual care is thought of and delivered and consider using revised ones for some purposes, especially for engaging individuals in their care.

The care clusters being introduced in mental health care in England need to support progressive developments in care. This is the first time the cluster descriptors have been critiqued from the perspectives of recovery orientation and co‐production.

This paper deals with the performance of port clusters. Port clusters are analyzed using a framework that draws from different schools that deal with clusters (see De Langen, 2004). Central to the framework is the identification of eight variables of cluster performance. Four of those-agglomeration and dis-agglomeration forces, internal competition, heterogeneity of the cluster and the level of entry and exit barriers-are related to the structure of a cluster and fourthe presence of trust, the presence of intermediaries, the presence of leader firms and the quality of collective action regimes-are related to the governance of clusters. The validity of these variables is confirmed in three case studies, of the port clusters of Rotterdam, Durban, and the lower Mississippi. The strengths and weaknesses of the three port clusters, the importance of the variables discussed above and opportunities for policy and management to improve the performance of clusters are discussed. The results of this study are relevant for cluster scholars and for scholars specializing in port studies and, since implications of this study for policy and management in (port) clusters are discussed, the study is also relevant for (port) cluster managers and for managers affirms in (port) clusters.

Data mining is the process of detecting knowledge from a given huge data set. Among the data set, multimedia is the data which contains diverse data such as audio, video, image, text and motion. In this growing field of video data, mining the video data plays vital role in the field of video data mining. In video data mining, video data are grouped into frames. In this vast amount of video frames, the fast retrieval of needed information is important one. This paper aims to propose a Birch-based clustering method for content-based image retrieval.

In image retrieval system, image segmentation plays a very important role. A text file, normally, is divided into sections, that is, piece, sentences, word and character for this information which are organized and indexed effectively like in a video, the information is dynamic in nature and this information is converted to static for easy retrieval. For this, video files are divided into a number of frames or segments. After the segmentation process, images are trained for retrieval process, and from these, unwanted images are removed from the data set. The noise or unwanted image removal pseudo-code is shown below. In the code image, pixel value represents the value of the difference between the two adjacent image pixel values. By assuming a threshold for the image value, the duplicate images are found. After finding the duplicate image, it is removed from the data set. Clustering is used in many applications as a stand-alone tool to get insight into data distribution and as a pre-processing step for other algorithms (Ester et al., 1996). Specifically, it is used in pattern recognition, spatial data analysis, image processing, economic science document classification, etc. Hierarchical clustering algorithms are classified as agglomerative or divisive. BRICH uses clustering attribute (CA) and clustering feature hierarchy (CA_Hierarchy) for the formation of clusters. It perform multidimensional data objects. Every BRICH algorithm based on the memory-oriented information, that is, memory constrains, is involved in the processing of the data sets. This information is represented in Figures 6-10. For forming clusters, they use the amount of object in the cluster (A), the sum of all points in the data set (S) and need the square value of the all objects (P).

The proposed technique brings an effective result for cluster formation.

BRICH uses a novel approach to model the degree of inter-connectivity and closeness between each pair of clusters that takes into account the internal characteristics of the clusters themselves.