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The draft Mental Health Bill 2004 proposes transfer of the main monitoring functions of the Mental Health Act Commission (MHAC) to the Healthcare Commission (or in practice whatever body succeeds the Healthcare Commission) with the abolition of the MHAC on implementation of the Bill when enacted. This paper describes the present role and remit of the Mental Health Act Commission, outlines the government's strategy on inspection and regulation and identifies the importance of protecting the rights of vulnerable adults and children with mental disorders. The reasons for retaining independent scrutiny and inspection of mental health services are explored and structures and mechanisms that might assist in achieving an effective regulatory environment are proposed.

In this article, Sasha Bhat describes a project in Bradford set up to improve mental health services by researching and designing better systems for involving black and minority ethnic (BME) communities in commissioning. Two members of the project, Kulvinder Kaur and Shabana Kauser, describe their reasons for joining, what they got out of it and what they hope will come out of it.

There are intense current debates about the place of belief systems in a secular society, and also whether the mechanistic approach to mental health care is sufficient for human beings. This paper aims to describe the Birmingham and Solihull Mental Health Foundation NHS Trust (BSMHFT) spirituality and mental health research programme within that context.

The research studies are placed within a discourse of current debates, but also within the specific context of the city of Birmingham. Birmingham is England's second city to London, and is an increasingly multi‐ethnic and multi‐cultural environment.

Those who use mental health services increasingly state that they wish to have the spiritual dimension of their lives attended to by professionals. The BSMHFT project reinforces this message and demonstrates the merits of close working with faith communities and engaging with staff in their understanding of spirituality.

The research by Professor Koenig et al. in the USA has demonstrated the physical and mental health benefits of belonging to a supportive faith community. The BSMHFT project is a rare UK example of research in this area and comes at a time of intense debate in England over the nature of society.

The K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.

This study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.

The performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.

The KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized employee and investor searching and other decision-making strategies in business.