In power systems, load curve data is one of the most important datasets that are collected and retained by utilities. The quality of load curve data, however, is hard to guarantee since the data is subject to communication losses, meter malfunctions, and many other impacts. In this paper, a new approach to analyzing load curve data is presented. The method adopts a new view, termed , on the load curve data by analyzing the periodic patterns in the data and re-organizing the data for ease of analysis. Furthermore, we introduce algorithms to build the virtual portrait load curve data, and demonstrate its application on load curve data cleansing. Compared to existing regression-based methods, our method is much faster and more accurate for both small-scale and large-scale real-world datasets.
In addition to the above methods, data mining techniques have also been developed to detect outliers, such as @math -nearest neighbor @cite_11 @cite_24 , @math -means @cite_16 @cite_1 , @math -medoids @cite_33 , density-based clustering @cite_9 , In general, these methods classify the observations with similar features, and find the observations that do not belong strongly to any cluster or far from other clusters. Nevertheless, most data mining techniques are designed for structured relational data, which may not align well for the need of outlier detection in load curve data. In addition, these methods are normally time consuming because they need a training process on a large dataset.
@cite_33 @cite_9 @cite_1 @cite_24 @cite_16 @cite_11
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Existing studies in data mining mostly focus on finding patterns in large datasets and further using it for organizational decision making. However, finding such exceptions and outliers has not yet received as much attention in the data mining field as some other topics have, such as association rules, classification and clustering. Thus, this paper describes the performance of control chart, linear regression, and Manhattan distance techniques for outlier detection in data mining. Experimental studies show that outlier detection technique using control chart is better than the technique modeled from linear regression because the number of outlier data detected by control chart is smaller than linear regression. Further, experimental studies shows that Manhattan distance technique outperformed compared with the other techniques when the threshold values increased. A common data mining task on networks is community detection, which seeks an unsupervised decomposition of a network into structural groups based on statistical regularities in the network's connectivity. Although many methods exist, the No Free Lunch theorem for community detection implies that each makes some kind of tradeoff, and no algorithm can be optimal on all inputs. Thus, different algorithms will over or underfit on different inputs, finding more, fewer, or just different communities than is optimal, and evaluation methods that use a metadata partition as a ground truth will produce misleading conclusions about general accuracy. Here, we present a broad evaluation of over and underfitting in community detection, comparing the behavior of 16 state-of-the-art community detection algorithms on a novel and structurally diverse corpus of 406 real-world networks. We find that (i) algorithms vary widely both in the number of communities they find and in their corresponding composition, given the same input, (ii) algorithms can be clustered into distinct high-level groups based on similarities of their outputs on real-world networks, and (iii) these differences induce wide variation in accuracy on link prediction and link description tasks. We introduce a new diagnostic for evaluating overfitting and underfitting in practice, and use it to roughly divide community detection methods into general and specialized learning algorithms. Across methods and inputs, Bayesian techniques based on the stochastic block model and a minimum description length approach to regularization represent the best general learning approach, but can be outperformed under specific circumstances. These results introduce both a theoretically principled approach to evaluate over and underfitting in models of network community structure and a realistic benchmark by which new methods may be evaluated and compared. Statistical assessment of the results of data mining is increasingly recognised as a core task in the knowledge discovery process. It is of key importance in practice, as results that might seem interesting at first glance can often be explained by well-known basic properties of the data. In pattern mining, for instance, such trivial results can be so overwhelming in number that filtering them out is a necessity in order to identify the truly interesting patterns. In this paper, we propose an approach for assessing results on real-valued rectangular databases. More specifically, using our analytical model we are able to statistically assess whether or not a discovered structure may be the trivial result of the row and column marginal distributions in the database. Our main approach is to use the Maximum Entropy principle to fit a background model to the data while respecting its marginal distributions. To find these distributions, we employ an MDL based histogram estimator, and we fit these in our model using efficient convex optimization techniques. Subsequently, our model can be used to calculate probabilities directly, as well as to efficiently sample data with the purpose of assessing results by means of empirical hypothesis testing. Notably, our approach is efficient, parameter-free, and naturally deals with missing values. As such, it represents a well-founded alternative to swap randomisation Data mining is a new, important and fast growing database application. Outlier (exception) detection is one kind of data mining, which can be applied in a variety of areas like monitoring of credit card fraud and criminal activities in electronic commerce. With the ever-increasing size and attributes (dimensions) of database, previously proposed detection methods for two dimensions are no longer applicable. The time complexity of the Nested-Loop (NL) algorithm (Knorr and Ng, in Proc. 24th VLDB, 1998) is linear to the dimensionality but quadratic to the dataset size, inducing an unacceptable cost for large dataset. A more efficient version (ENL) and its parallel version (PENL) are introduced. In theory, the improvement of performance in PENL is linear to the number of processors, as shown in a performance comparison between ENL and PENL using Bulk Synchronization Parallel (BSP) model. The great improvement is further verified by experiments on a parallel computer system IBM 9076 SP2. The results show that it is a very good choice to mine outliers in a cluster of workstations with a low-cost interconnected by a commodity communication network.