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A Wireless Sensor Network (WSN) is a network of small sensor nodes which are energy constraint devices and have limited data transmission and computational power. Clustering is an important mechanism in large multi-hop wireless sensor networks for obtaining scalability, reducing energy consumption and achieving better network performance. Most of the research in this area has focused on energy-efficient solutions, but has not thoroughly analyzed the network performance, e.g. in terms of data collection rate and time. In this paper we are presenting the clustering of wireless sensor network by using k-**means** approach, over a large dynamic network. As it is the oldest and simplest **method** of clustering. This **method** requires only local communication and synchronization. Due to growing in area of peer to peer and mobile sensor networks, data analysis in large, dynamic network in large garner importance in the near future. Our algorithm shows best result for the large dynamic network. We tested our algorithm in a simulated environment up to 100 nodes in a dynamic environment and analyze its behavior with good accuracy.

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6. CONCLUDING REMARKS
In this paper, we settled the smoothed running time of the k-**means** **method** for ar- bitrary k and d. The exponents in our smoothed analysis are constant but large. We did not make a huge effort to optimize the exponents as the arguments are intricate enough even without trying to optimize constants. Furthermore, we believe that our approach, which is essentially based on bounding the smallest possible improvement in a single step, is too pessimistic to yield a bound that matches experimental obser- vations. A similar phenomenon occurred already in the smoothed analysis of the 2-opt heuristic for the TSP [Englert et al. 2007]. There it was possible to improve the bound for the number of iterations by analyzing sequences of consecutive steps rather than single steps. It is an interesting question if this approach also leads to an improved smoothed analysis of k-**means**.

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We present polynomial upper and lower bounds on the number of iterations per- formed by the k-**means** **method** (a.k.a. Lloyd’s **method**) for k-**means** clustering. Our upper bounds are polynomial in the number of points, number of clusters, and the spread of the point set. We also present a lower bound, showing that in the worst case the k-**means** heuristic needs to perform Ω(n) iterations, for n points on the real line and two centers. Surprisingly, the spread of the point set in this construction is poly- nomial. This is the first construction showing that the k-**means** heuristic requires more than a polylogarithmic number of iterations. Furthermore, we present two alternative algorithms, with guaranteed performance, which are simple variants of the k-**means** **method**. Results of our experimental studies on these algorithms are also presented.

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WSN consist of hundreds of thousands of small and cost effective sensor nodes. Sensor nodes are used to sense the environmental or physiological parameters like temperature, pressure, etc. For the connectivity of the sensor nodes, they use wireless transceiver to send and receive the inter-node signals. Sensor nodes, because connect their selves wirelessly, use routing process to route the packet to make them reach from source to destination. These sensor nodes run on batteries and they carry a limited battery life. Clustering is the process of creating virtual sub-groups of the sensor nodes, which helps the sensor nodes to lower routing computations and to lower the size routing data. There is a wide space available for the research on energy efficient clustering algorithms for the WSNs. LEACH, PEGASIS and HEED are the popular energy efficient clustering protocols for WSNs. In this research, we are working on the development of a hybrid model using LEACH based energy efficient and K-**means** based quick clustering algorithms to produce a new cluster scheme for WSNs with dynamic selection of the number of the clusters automatically. In the proposed **method**, finding an optimum „k‟ value is performed by Elbow **method** and clustering is done by k-**means** algorithm, hence routing protocol LEACH which is a traditional energy efficient protocol takes the work ahead of sending data from the cluster heads to the base station. The results of simulation show that at the end of some certain part of running the proposed algorithm, at some point the marginal gain will drop dramatically and gives an angle in the graph. The correct „k‟

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assumptions underlying the two factor-scaling methods may affect the power of the LRT κ .
Neither model size nor model complexity was varied in this study. For simplicity, a two-group, one-factor CFA model with six indicator variables was the true generating model. Future researchers could consider more complex models (for example, more observed indicators and/or additional latent variables) to investigate whether varying the model size and/or model complexity would affect the **testing** and description of the latent mean difference across groups. Future research that includes models with more observed indicators could likewise investigate more severe loading non-invariance conditions. Further, mean comparisons between more than two groups are not uncommon and, hence, the impact of including more than two groups on latent mean comparisons could be examined in future investigations. In addition, multivariate normal data were generated. Future studies could also explore the implications of violating the assumption of normality when using the

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However, the extension of the logarithm, identric and Seiﬀert **means** from two to three or more variables does not appear to be obvious from the above expressions of these **means**.
In this sense, we refer the reader to [, –] for some extensions about the logarithmic and identric **means**. Here, we will derive other extensions of these latter **means** from our above study. In fact, the above transformation for **means** with two variables can be imme- diately stated in a similar manner for **means** involving several variables. For instance, we can deﬁne

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Hypothesis **testing** is a **method** of making statistical decision using experimental data. One use of hypothesis **testing** is in deciding whether experimental result contains enough information to cast doubt on conventional wisdom. Hypothesis **testing** are performed by many researcher in various fields of inquiry, usually to discover something about particular process. Literally, hypothesis **testing** is a **method** of **testing** a claim about a parameter in a population, using data measured in a sample[3,4]. In this **method**, we test some claim by determine a likelihood that a sample statistic could have been selected if the hypothesis regarding the population parameter were true.

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The proposed formula contains upper capital limit which is designed to protect the tax-payer. A problem with capital limits is that they introduce complexity and change behaviour, in this case by encouraging people to spend down or give away their assets in order to stay within them. In the proposed **method** a cut-off of £118,000 will restrict state funding to those with assets below this figure. In the preferred **method**, by contrast, the upper limit is different for each care tariff. Because it is not based on a single global value, it should discourage the early disposal assets since the tariff they receive will not be known in advance. For cost control purposes, we therefore believe a tariff system based on the care package and not an arbitrary upper limit is hence a better and more logical approach.

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Figure 3. Water content versus effective normal stresses before and after shearing.
Figure 4. Definition of the Hvorslev criterion.
behaves elasto-plastically under effective stresses. The cohesion of saturated **soil** is a function of its water con- tent. But since the water content is proportional to the void ratio at saturation, the cohesion is also a function of the void ratio. That **means** that two specimens with different degrees of over-consolidation have different void ratios and thus their respective cohesions are varied. However, shear box tests have traditionally been carried out without considering the state of over-consolidation of the clay. This introduces inconsistencies, which may be illustrated by the following hypothetical example. Consider two **soil** specimens, which have been taken from the same depth and are being tested in the shear box under different normal stress levels. Since both specimens were surcharged equally, they have different states of over-consolidation during the **testing**. The results of such tests are frequently used to define a single failure envelope. Since the specimens behave differently under these test- ing conditions, and thus have different values of true cohesion, fitting them on one line is contradictory. This line will vary in relation to the **testing** conditions. Shear parameters, which are obtained from randomly distri- buted results of shear tests that depend upon contingencies are fictitious.

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NCR-13 wants it clearly understood that the publication of these tests and procedures in no way implies that the ultimate has been reached. Research and innovation on methods of **soil** **testing** should con- tinue. The committee strongly encourages increased research efforts to devise better, faster, less expensive and more accurate **soil** tests. With the high cost of fer- tilizer, and with the many **soil** related environmental concerns, it is more important than ever that fertilizer be applied only where needed and in the amount of each element needed for the response goal. The best hope of attaining this goal is better **soil** tests and bet- ter correlations with plant response. NCR-13 stands ready to evaluate promising new **soil** tests, and with clear justification will move quickly to revise their rec- ommendations.

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The proposed formula contains an upper capital limit which is designed to protect the tax payer. A problem with capital limits is that they introduce complexity and change behaviour, in this case by encouraging people to spend down or give away their assets in order to stay within them. In the proposed **method**, a cut-off of £118,000 will restrict state funding to those with assets below this figure. In the preferred **method**, by contrast, the upper limit is different for each care tariff. Because it is not based on a single global value, it should discourage the early disposal of assets, since the tariff they receive will not be known in advance. For cost control purposes, we therefore believe a tariff system based on the care package and not an arbitrary upper limit is a better and more logical approach.

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4.3 Particle Size Analysis
Particle size analysis will be performed by **means** of sieving (ASTM D 422). For oven-dry materials, sieving is carried out for particles that are being retained on a 0.063 mm sieve. In sieve analysis, the mass of **soil** retained on each sieve is determined and expressed as a percentage of the total mass of the sample. The particle size is plotted on a logarithmic scale so that two soils having the same degree of uniformity are represented by curves of the distribution plot. In Hydrometer analysis is based on the principle of sedimentation of **soil** grains in water. When a **soil** specimen is dispersed in water, the particles settle at different velocities, depending on their shape, size, and weight. For simplicity, it is assumed that **soil** particles are spheres and the velocity of **soil** particles can be express by Stokes’ law.

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In this chapter we provided a partial evaluation of the lifeline or increas- ing block tariff electricity subsidy in Honduras. With funding from the gov- ernment, the public utility is offering electricity at greatly subsidized rates for those households with monthly consumption below 300 kWh. Because the lifeline threshold is set so high, 83.5 percent of the utility’s residential clients benefit from the subsidy. At the same time, 81.8 percent of the sub- sidy may well be spent on nonpoor households. While this last statistic could be lower if we were using a different **method** for measuring poverty, it remains true that the impact on poverty of the subsidy is rather small in comparison to its cost. The fact that the current subsidy is badly targeted does not mean that it could not be improved by reducing the lifeline thresh- old. A lower lifeline subsidy as currently being considered by the govern- ment would have the potential of being more effective. Alternative proxy

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Blevins and Massey (4) indicate, however, that millet, did not show typical copper deficiency symptoms when grown in the greenhouse on soil containing 0.2 ppm EDTA-extractable copper, al[r]

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Soil Testing Is an Excellent Investment for Garden, Lawn, and Landscape Plants, and Commercial Crops-page 2?. Figure 2.[r]