in KDD systems. Why Do We Need KDD? The traditional method of turning data into knowledge relies on manual analysis and interpretation. For example, in the health-care industry, it is common for specialists to periodically analyze current trends and changes in health-care data, say, on a quarterly basis

Abstract: We use the National Family Health Survey (NFHS) data collected in Indian states in 1992 and 1993 to estimate the relationship between household wealth and the probability a child (aged 6 to 14) is enrolled in school. A methodological difficulty to overcome is that the NFHS, modeled

present a set of experiences from a four month long deployment on a remote island. We analyze the environmental and node health data to evaluate system performance. The close integration of WSNs with their environment provides environmental data at densities previously impossible. We show that the sensor

Many prominent theorists have argued that accurate perceptions of the self, the world, and the future are essential for mental health. Yet considerable research evidence suggests that overly positive selfevaluations, exaggerated perceptions of control or mastery, and unrealistic optimism

economics, rationality Most real decisions, unlike those of economics texts, have a status quo alternative-that is, doing noth-ing or maintaining one’s current or previous decision. A series of decision-making experiments shows that individuals disproportionately stick with the status quo. Data

such as temperature, light, or seismic sensors with networking and computation capabilities. Applications range from environmental control, warehouse inventory, and health care to military environments. Existing sensor networks assume that the sensors are preprogrammed and send data to a central frontend where

Content analysis is a widely used qualitative Researchers regard content analysis as a flexible method for analyzing text data The differentiation of content analysis is usually limited to classifying it as primarily a qualitative versus quantitative research method. A more thorough analysis

The Satisfaction With Life Scale (SWLS) was developed to assess satis-faction with the respondent’s life as a whole. The scale does not assess satisfaction with life domains such as health or finances but allows subjects to integrate and weight these domains in whatever way they choose. Normative

contingent-claims framework. In Section II, we discuss the data and define the proxies used. In Section III, we analyze our results. In Section IV, we provide evidence for the robustness of our results on several fronts. First, we repeat the analysis using transactions (rather than quotes) data to obtain

that the DP model is able to account for wide variety of phenomena observed in the data, including the pronounced peaks in the distribution of retirement ages at 62 and 65 (the ages of early and normal eligibility for Social Security benefits, respectively). We identify a significant fraction of “health